IMAGES

EARLIEST PRIORITY DATE: This Application claims priority from a Provisional patent application filed in India having Patent Application No. 202041020407, filed on May 14, 2020, and titled “A SYSTEM TO DIAGNOSE ONE OR MORE DISEASES”.

FIELD OF INVENTION

Embodiments of the present disclosure relate to image processing and more particularly to a system and a method for diagnosis of diseases from medical images.

BACKGROUND

Early and accurate diagnose of a disease is critically important because diagnoses may improve effectiveness of a treatment and avoid a long-term complication for a patient. Moreover, undiagnosed, and incorrect diagnose of the patient may unknowingly transmit the disease to others. In today’s scenario, many diseases have taken a toll on whole world, where X-Ray and CT images should be studied by a radiologist and the radiologist has to manually identify the possibility of having a particular disease in the individual. Manual interpretation is a huge challenge to radiologist to identify whether or not the individual is affected by a particular disease. This is because of the near similarity of X-Ray /CT images of one disease may match with any other similar diseases.

In an example of such cases if we consider corona virus disease, more specifically, COVID-19, has one of the current gold standards for testing and diagnosis of COVID-19 as RT-PCR test which however has some limitations. RT-PCR is not free from false negatives (FN). The false negative value may be due to several reasons like sampling location of the swab test may be incorrect, unexpected procedural error or unavailability of correct samples. Thus, in the majority of studies, it has been reported that RT-PCR test yields high specificity, but a low sensitivity. Another disadvantage of RT-PCR test is the time it consumes to yield the result of the test. One more dis-advantage of this test, is the shortage of available testing kits in the beginning of pandemic in most of the countries. This may eventually result in preventing infection containment and patient isolation, the failure of which would lead to the fast spread of disease resulting in community transfer. Though rapid testing kits have been used but they reported failure interims of quality and test results.

Furthermore, one of the more efficient technique involve detection of corona virus using X-Ray and CT images. However, in such technique, currently radiologists are not able to identify differences or new patterns of COVID-19 which differentiates it with other lung infections such as SARS, ARDS, Pneumonia and other respiratory tract infections. Presently, there is no diagnosis testing tool that has high efficacy interims of accuracy and precision with high true positive rate (TPR) and true negative rate (TNR) and less false positive rate (FPR) and false negative rate (FNR). Hence, there is a need for an improved system and a method for diagnosis of diseases to address the aforementioned issue(s).

BRIEF DESCRIPTION

In accordance with an embodiment of the present disclosure, a system for diagnosis of diseases from medical images is provided. The system includes a processing subsystem hosted on a server and configured to execute on a network to control bidirectional communications among a plurality of modules. The processing subsystem includes an image acquisition module configured to receive an image of a diseased area of a human body from a diagnostic medical device. The processing subsystem also includes a resolution conversion module configured to convert resolution of the image received by the image acquisition module into a predefined resolution to obtain a sample image. The processing subsystem further includes an image preprocessing module configured to obtain an intensity value matrix upon converting the sample image into gray scale image, wherein the intensity value matrix is corresponding to numeric value of pixels of the gray scale image. The image preprocessing module is also configured to obtain a testing data set by converting the intensity value matrix into a test row vector. The processing subsystem further includes a dimensionality reduction module configured to generate a dimensionality reduced test matrix based on the testing data set obtained from image preprocessing module and a transformation training matrix obtained from a training data set. The processing subsystem further includes a similarity identification module configured to identify a similarity value of each row vector of the dimensionality reduced test matrix obtained from the dimensionality reduction module with a dimensionality reduced training matrix obtained from the transformation training matrix using a similarity function. The processing subsystem further includes a disease classification module configured to assign a class label of a training row vector to the test row vector based on a maximum similarity value obtained from the similarity identification module. The disease classification module is also configured to identify a type of disease present in the image of diseased area of a human body based on the class label classified by the disease classification module.

In accordance with another embodiment of the present disclosure, a method for diagnosis of diseases from medical images is provided. The method includes receiving, by an image acquisition module, an image of a diseased area of a human body from a diagnostic medical device. The method includes converting, by a resolution conversion module, resolution of the image received by the image acquisition module into a predefined resolution to obtain a sample image. The method includes obtaining, by an image preprocessing module, an intensity value matrix upon converting the sample image into gray scale image, where the intensity value matrix is corresponding to numeric value of pixels of the gray scale image. The method includes obtaining, by the image preprocessing module, a testing data set by converting the intensity value matrix into a test row vector. The method further includes generating, by a dimensionality reduction module, a dimensionality reduced test matrix based on the testing data set obtained from image preprocessing module and a transformation training matrix obtained from a training data set. The method further includes identifying, by a similarity identification module, a similarity value of each row vector of the dimensionality reduced test matrix obtained from the dimensionality reduction module with a dimensionality reduced training matrix obtained from the transformation training matrix using a similarity function. The method further includes assigning, by a disease classification module, a class label of a training row vector to the test row vector based on a maximum similarity value obtained from the similarity identification module. The method further includes identifying, by the disease classification module, a type of disease present in the image of diseased area of a human body based on the class label classified by the disease classification module.

To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the disclosure and are therefore not to be considered limiting in scope. The disclosure will be described and explained with additional specificity and detail with the appended figures.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which: FIG. 1 is a block diagram representation of one embodiment of the system for diagnosis of disease from medical images depicting training procedure of the system in accordance with an embodiment of the present disclosure;

FIG. 2 is a block diagram representation of system for diagnosis of disease from medical image, depicting a testing procedure in a real-time scenario of a disease in accordance with an embodiment of the present disclosure;

FIG. 3 is a schematic representation of an exemplary system for diagnosis of diseases of FIG. 2 in accordance with an embodiment of the present disclosure;

FIG. 4 is a block diagram of a computer or a server for system for kiddie rides in accordance with an embodiment of the present disclosure; and FIG. 5 is a flow chart representing the steps involved in a method for diagnosis of diseases from medical images in accordance with an embodiment of the present disclosure. Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein.

DETAILED DESCRIPTION

For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure.

The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such a process or method. Similarly, one or more devices or sub-systems or elements or structures or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other devices, sub-systems, elements, structures, components, additional devices, additional sub-systems, additional elements, additional structures or additional components. Appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting. In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. The singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise.

Embodiments of the present disclosure relate to a system and a method for diagnosis of diseases from medical images is provided. The system includes a processing subsystem hosted on a server and configured to execute on a network to control bidirectional communications among a plurality of modules. The processing subsystem includes an image acquisition module configured to receive an image of a diseased area of a human body from a diagnostic medical device. The processing subsystem also includes a resolution conversion module configured to convert resolution of the image received by the image acquisition module into a predefined resolution to obtain a sample image. The processing subsystem further includes an image preprocessing module configured to obtain an intensity value matrix upon converting the sample image into gray scale image, wherein the intensity value matrix is corresponding to numeric value of pixels of the gray scale image. The image preprocessing module is also configured to obtain a testing data set by converting the intensity value matrix into a test row vector. The processing subsystem further includes a dimensionality reduction module configured to generate a dimensionality reduced test matrix based on the testing data set obtained from image preprocessing module and a transformation training matrix obtained from a training data set. The processing subsystem further includes a similarity identification module configured to identify a similarity value of each row vector of the dimensionality reduced test matrix obtained from the dimensionality reduction module with a dimensionality reduced training matrix obtained from the transformation training matrix using a similarity function. The processing subsystem further includes a disease classification module configured to assign a class label of a training row vector to the test row vector based on a maximum similarity value obtained from the similarity identification module. The disease classification module is also configured to identify a type of disease present in the image of diseased area of a human body based on the class label classified by the disease classification module. FIG. 1 is a block diagram representation of one embodiment of the system (10) for diagnosis of disease from medical images depicting training procedure of the system in accordance with an embodiment of the present disclosure. The system (10) includes a training model which needs to be trained for real time diagnosis of diseases from medical images. The system is configured to receive a plurality of training images corresponding to the diseased area of a human body from a corresponding plurality of diagnostic medical devices in step 20. In one embodiment, the plurality of images may include at least one of an X-ray image, a CT scan image, an MRI scan image or the like. In such an embodiment, the plurality of images may be a normal x-radiation (X-RAY) or a normal computerized tomography (CT) image, a corona virus (COVID-19) X-RAY or a corona virus CT image, a respiratory tract infection image, a lung opacity image, a severe acute respiratory syndrome (SARS) image, an acute respiratory distress syndrome (ARDS) image, and the like. In a specific embodiment, the plurality of images may be directly obtained from the diagnostic medical devices such as at least one of an X-ray machine, a CT scan machine or an MRI machine or the like.

Once the plurality of images is obtained, the system converts resolution of the plurality of training images into a predefined resolution to obtain a plurality of model training images in step 30. For example, every image in the plurality of training images have a different resolution type. Hence, each image of the plurality of training images are converted into the predefined resolution to have same resolution type. In an exemplary embodiment, the plurality of training images may be converted to a 244*244-pixel size to make the plurality of training images to the same resolution. Further, the system converts the plurality of model training images into gray scale in step 40. As used herein, grayscale image is the image in which the only colors are shades of gray. The conversion of a color image into a grayscale image includes converting the RGB values (24 bit) into grayscale value (8 bit).

Furthermore, the plurality of gray scale images is converted into a plurality of training intensity value matrix, where the values in the plurality of training intensity value matrix represent intensity value of each pixel of the corresponding plurality of gray scale images in step 50. The intensity value represents numerical values of each pixel of the gray scale image. Subsequently, the plurality of training intensity value matrix is converted into a plurality of training row vector in step 60, where each of the plurality of training row vector is assigned with a corresponding plurality of class labels in step 70. As used herein, the class labels represent the set of images from the plurality of training images belonging to a particular category of disease. In an exemplary embodiment, the class labels may include a class of normal condition without any disease , a class of corona virus (COVID- 19) disease, a class of respiratory tract infection, a class of lung opacity disease, a class of severe acute respiratory syndrome (SARS), a class of an acute respiratory distress syndrome (ARDS) and the like. The group with the plurality of training row vectors along with corresponding class labels is prepared as a training data set in step 80.

Moreover, the training data set is used to train the training model, where the training model is an artificial intelligence based model or a machine learning based model. The training data set is used to calculate posterior probability of each column of one or more sets of training row vectors corresponding to the plurality of class labels in step 90. Further, the image feature pattern vector is obtained based on calculated posterior probability, where the image feature pattern vector is corresponding to relation of features of the image with class labels in step 100. The order of the image feature pattern vector includes resolution*class labels. For example, if the data set includes a normal image and a corona virus disease image, then the image feature pattern vector includes a dimensionality equal to two, where first element corresponds to normal class and the second element of the vector corresponds to corona virus class. Here, the dimension of each element of the image feature pattern vector is equal to the number of class labels. The image feature pattern vector describes the probability that a given feature of the image belongs to a particular class label.

Furthermore, the system performs an incremental clustering technique on the image feature pattern vector based on a similarity function in step 110. In detail, at least two vectors of the image feature pattern vector are selected to find the similarity between the two vectors. The at least two vectors are compared with a predefined input similarity value. As a result, if the similarity values of the at least two vector is equal or greater than the predefined input similarity value then, the at least two vectors are considered of same class and clustered together otherwise, the at least two vectors are clustered into two different clusters. Consequently, the system generates an individual cluster mean and deviation for each column of the image feature pattern vector based on one or more input parameters in step 120. The one or more input parameter may include at least one of a similarity value, a standard deviation or a combination thereof. The system further generates membership matrix based on the image feature pattern vector and similarity value in step 140. The membership matrix includes image feature pattern vector on the row side and similarity value of the clusters corresponding to the elements of the image feature pattern vector.

Further, the system obtains a transformation training matrix by converting the membership matrix into binary form matrix by comparing similarity value of each column of the membership matrix with the input similarity value in step 150. For example, the membership matrix having similarity values as [0.64, 0.48, 0.76] and the input similarity value is 0.64, then the values which are equal or greater than the input similarity values are considered as binary 1 and others are considered as binary 0. Hence, the transformation training matrix will be [1 0 1]. The training data set and the transformation training matrix are used to obtain a dimensionality reduced training matrix in step 160. In details, the training data set is multiplied with the transformation training matrix to obtain dimensionality reduced matrix which is a noise free matrix of precise data. The dimension of the dimensionality reduced matrix is the resolution*number of clusters as the dimension of the training data set is resolution*resolution and the dimension of the transformation training matrix is resolution*number of clusters. The transformation training matrix and the training data set are used to train the model for diagnosis of diseases from the medical images.

FIG. 2 is a block diagram representation of system (10) for diagnosis of disease from medical image, depicting a testing procedure in a real-time scenario of a disease in accordance with an embodiment of the present disclosure. The system (10) includes a processing subsystem (200) hosted on a server (210). The processing subsystem is configured to execute on a network to control bidirectional communications among a plurality of modules. In one embodiment, the server may include a cloud based server. In another embodiment, the server may include a local server. In a specific embodiment, the network may be a wired network such as local area network (LAN). In another embodiment, the network may be a wireless network such as wi-fi, Bluetooth ^R low energy (BLE) communication, Zigbee ^R communication, near field communication (NFC) and the like. The plurality of modules of the processing subsystem are trained with the training model.

The processing subsystem includes an image acquisition module (220) which is configured to receive an image of a diseased area of a human body from a diagnostic medical device. In one embodiment, the image may include an X-ray image, a CT scan image or an MRI scan image of a disease affected area of the human body. The image is directly obtained from the diagnostic medical device. The image obtained from the diagnostic medical device may be stored in a database of the system. Further, the processing system includes a resolution conversion module (230) which is configured to convert resolution of the image received by the image acquisition module into a predefined resolution to obtain a sample image. The image may be converted into the predefined resolution as per the user requirement.

Furthermore, the processing subsystem includes an image preprocessing module (240) which is configured to obtain an intensity value matrix upon converting the sample image into gray scale image. The intensity value matrix is corresponding to numeric value of pixels of the gray scale image. The image preprocessing module is further configured to obtain a testing data set by converting the intensity value matrix into a test row vector. The process is similar to that of training data set of FIG. 1. Further, the processing subsystem includes a dimensionality reduction module (250) which is configured to generate a dimensionality reduced test matrix based on the testing data set obtained from image preprocessing module and the transformation training matrix obtained from a training data set used to train the training model of FIG. 1. More specifically, the testing data set and the transformation training matrix are multiplied to obtain the dimensionality reduced test matrix.

Subsequently, the processing subsystem further includes a similarity identification module (260) which is configured to identify a similarity value of each row vector of the dimensionality reduced test matrix obtained from the dimensionality reduction module with the dimensionality reduced training matrix obtained from the system of FIG. 1. In one embodiment, the similarity identification module may be configured to identify a similarity value of each row vector of the dimensionality reduced test matrix with a dimensionality reduced training matrix based on each individual class label. The processing subsystem further includes a disease classification module (270) which is configured to assign a class label of a training row vector to the test row vector based on a maximum similarity value obtained from the similarity identification module. The disease classification module is also configured to identify a type of disease present in the image of diseased area of a human body based on the class label classified by the disease classification module.

FIG. 3 is a schematic representation of an exemplary system (10) for diagnosis of diseases of FIG. 2 in accordance with an embodiment of the present disclosure. Considering an example where the artificial intelligence models or machine learning models of the system are trained with the X-ray, CT or MRI images of different kind of diseases such as corona virus (COVID-19) disease, respiratory tract infection, lung opacity disease, severe acute respiratory syndrome (SARS), an acute respiratory distress syndrome (ARDS) and the like. Also, the system is trained to identify the X-ray, CT or MRI images of a normal person having no diseases. To train the model, 15 images of Covid-19, normal and viral pneumonia are considered, where the input parameters may include threshold value of similarity: 0.99995 and assumed standard deviation: 0.5. The system performs various image pre-processing steps on the collected images. The resolution conversion module converts the collected image into 244*244 pixel size as per the requirement of the system and operator of the system. The image preprocessing module converts the reduced resolution image into gray scale image and obtain an intensity value matrix, where the elements of the matrix are intensity values corresponding to numeric value of pixels of the gray scale image. Further, the intensity value matrix is converted to obtain a row vector, where row vector is assigned with class labels. Moreover, the collected images are converted into a training dataset matrix of 15 images x 25 dimensions followed by class label of the image as shown below in table- 1 and table 2:

Table- 1

Table-2

The system further obtains an image feature pattern vector based on calculated posterior probability, where the image feature pattern vector is corresponding to relation of features of the image with class labels. The posterior probability of each dimension/pixel with respect to class label (25 dimensions and 3 classes). Hence, the image feature pattern vector is 25 dimension x 3 classes matrix.

Table- 3

Furthermore, the system performs an incremental clustering technique on the image feature pattern vector based on the similarity function to obtain individual clusters. Consider that the row vector 1 as initial cluster mean center and standard deviation as 0.5, the system calculates similarity between row vector 1 (initial mean) and new vector using similarity function.

For a New row vector if number of clusters = 1 , then Calculate similarity between mean of cluster 1 and new vector using the similarity function

If similarity measure >= threshold then new row vector into row vector 1 cluster is added recompute mean center (Number of clusters = 1 ) New cluster with new row vector as mean center is created if number of clusters > 1 , then

Calculate similarity between mean of each cluster and new vector using novel similarity measure

If similarity measure >= threshold then new row vector into the cluster with highest similarity and recompute mean is calculated

New cluster with New row vector as mean center is calculated

After the clustering process, the system computes cluster means and cluster standard deviation. The clusters are [[0, 17], [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11, 12], [13], [14], [15], [16], [18], [19], [20], [21, 22], [23], [24]] cluster means and deviation (22

Clusters x 3 classes) as shown in table-3

Table-4

Subsequently, the system calculates similarity between each the image feature pattern vector with cluster means using calculated standard deviations using the similarity function. The system generates membership matrix based on the image feature pattern vector and similarity value.

Similarity of (25 dimensions x 3 classes) and (22 Clusters x 3 classes) = Membership matrix order - 25 dimensions x 22 clusters

Table-5

The system further converts the membership matrix into binary form matrix by comparing similarity value of each column of the membership matrix with a threshold similarity value, where the binary form matrix is corresponding to the transformation training matrix.

If membership value >= Threshold, assign 1 as value Transformation matrix order - 25 dimensions x 22 clusters Table-6

The system further obtains the dimensionality reduction training matrix based on the training data set and the transformation training matrix.

Training dataset x Transformation Matrix (15 images x 25 dimensions) x (25 dimensions x 22 clusters) = (15 images x 22 Clusters) - DR matrix

Table-7

In real-time consider that a person X and person Y are suffering from some disease and the image acquisition module (220) obtains two images of the lungs of the person X and person Y to identify the diseases. The resolution conversion module (230) further converts the resolution of the images obtained from the image pre-processing module. The image preprocessing module (240) converts it into gray scale, converts into an intensity value matrix and obtains a row vector from the intensity value matrix as shown in below table - 8:

Table-8 The dimensionality reduction module (250) generates a dimensionality reduced test matrix based on the testing data set obtained from image preprocessing module and a transformation training matrix obtained from a training data set as shown in table-9

Testing dataset x Transformation Matrix (2 images x 25 dimensions) x (25 dimensions x 22 clusters) = (2 images x 22 Clusters) - DR Test matrix

Table-9

Furthermore, the similarity identification module (260) identifies a similarity value of each row vector of the dimensionality reduced test matrix obtained from the dimensionality reduction module with a dimensionality reduced training matrix obtained from the transformation training matrix using a similarity function. The disease classification module (270) assigns a class label of a training row vector to the test row vector based on a maximum similarity value obtained from the similarity identification module. The disease classification module identifies a type of disease present in the image of diseased area of a human body based on the class label classified by the disease classification module.

Test Image 1 suspected to have Class: Covid Similarity:86.91245616411727 Class Covid: 86.91245616411727 Class Normal: 69.183611233574 Class Viral pneumonia: 69.44842593981684 Test Image2 suspected to have Class: Covid Similarity:83.21954045729483 Class Covid: 83.21954045729483 Class Normal: 65.38575516732506 Class Viral pneumonia: 69.17832077555704 Testing (Cross Validation): Foldl:

Number of Training Instances7 Number of Testing Instances8

Fold2:

Number of Training Instances8 Number of Testing Instances? Confusion Matrix:

5 00

03 2

03 2

Classl.0: Accuracy: 100.0% Precision: 100.0% Class2.0: Accuracy: 66.66666666666666% Precision: 50.0%

Class3.0: Accuracy: 66.66666666666666% Precision: 50.0%

Total Accuracy :77.77777777777779 %

Correctly Classified %66.66666666666666 Incorrectly Classified %33.33333333333334 FIG. 4 is a block diagram of a computer or a server (300) for system for kiddie rides in accordance with an embodiment of the present disclosure. The server includes processor(s) (310), and memory (320) operatively coupled to the bus (330).

The processor(s) (310), as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or any other type of processing circuit, or a combination thereof. The memory (320) includes a plurality of subsystems and a plurality of modules stored in the form of executable program which instructs the processor (310) to perform the method steps illustrated in FIG. 1. The memory (320) is substantially similar to the system (10) of FIG.l. The memory (320) has following subsystems: the processing subsystem (200) including image acquisition module (220), a resolution conversion module (230), an image preprocessing module (240), a dimensionality reduction module (250), a similarity identification module (260) and a disease classification module (270).

The processing subsystem includes an image acquisition module configured to receive an image of a diseased area of a human body from a diagnostic medical device. The processing subsystem also includes a resolution conversion module configured to convert resolution of the image received by the image acquisition module into a predefined resolution to obtain a sample image. The processing subsystem further includes an image preprocessing module configured to obtain an intensity value matrix upon converting the sample image into gray scale image, wherein the intensity value matrix is corresponding to numeric value of pixels of the gray scale image. The image preprocessing module is also configured to obtain a testing data set by converting the intensity value matrix into a test row vector. The processing subsystem further includes a dimensionality reduction module configured to generate a dimensionality reduced test matrix based on the testing data set obtained from image preprocessing module and a transformation training matrix obtained from a training data set. The processing subsystem further includes a similarity identification module configured to identify a similarity value of each row vector of the dimensionality reduced test matrix obtained from the dimensionality reduction module with a dimensionality reduced training matrix obtained from the transformation training matrix using a similarity function. The processing subsystem further includes a disease classification module configured to assign a class label of a training row vector to the test row vector based on a maximum similarity value obtained from the similarity identification module. The disease classification module is also configured to identify a type of disease present in the image of diseased area of a human body based on the class label classified by the disease classification module. Computer memory elements may include any suitable memory device(s) for storing data and executable program, such as read only memory, random access memory, erasable programmable read only memory, electrically erasable programmable read only memory, hard drive, removable media drive for handling memory cards and the like. Embodiments of the present subject matter may be implemented in conjunction with program modules, including functions, procedures, data structures, and application programs, for performing tasks, or defining abstract data types or low-level hardware contexts. Executable program stored on any of the above-mentioned storage media may be executable by the processor(s) (310). FIG. 5 is a flow chart representing the steps involved in a method (400) for diagnosis of diseases from medical images in accordance with an embodiment of the present disclosure. The method includes receiving, by an image acquisition module, an image of a diseased area of a human body from a diagnostic medical device in step 410. In one embodiment, the image may include an X-ray image, a CT scan image or an MRI scan image. In a specific embodiment, the diagnostic medical device may include at least one of an X-ray machine, a CT scan machine or an MRI machine. In one embodiment, the plurality of images may include at least one of an X-ray image, a CT scan image, an MRI scan image or the like. In some embodiments, the plurality of images may be a normal x- radiation (X-RAY) or a normal computerized tomography (CT) image, a corona virus (COVID-19) X-RAY or a corona virus CT image, a respiratory tract infection image, a lung opacity image, a severe acute respiratory syndrome (SARS) image, an acute respiratory distress syndrome (ARDS) image, and the like.

The method includes converting, by a resolution conversion module, resolution of the image received by the image acquisition module into a predefined resolution to obtain a sample image in step 420. In an exemplary embodiment, the images may be converted to a 244*244-pixel size to make the images to the same resolution as per user requirement. The method includes obtaining, by an image preprocessing module, an intensity value matrix upon converting the sample image into gray scale image, where the intensity value matrix is corresponding to numeric value of pixels of the gray scale image in step 430. In a specific embodiment, the conversion of a color image into a grayscale image includes converting the RGB values (24 bit) into grayscale value (8 bit).

The method includes obtaining, by the image preprocessing module, a testing data set by converting the intensity value matrix into a test row vector in step 440. The method further includes generating, by a dimensionality reduction module, a dimensionality reduced test matrix based on the testing data set obtained from image preprocessing module and a transformation training matrix obtained from a training data set in step 450. In one embodiment, obtaining a training data set includes receiving a plurality of training images corresponding to the diseased area of a human body from a corresponding plurality of diagnostic medical devices. In such an embodiment, obtaining a training data set includes converting resolution of the plurality of training images into a predefined resolution to obtain a plurality of model training images. In such an embodiment, obtaining a training data set includes obtaining a plurality of training intensity value matrix upon converting the plurality of model training images into a plurality of gray scale images. In such an embodiment, obtaining a training data set includes converting the plurality of training intensity value matrix into a plurality of training row vector. In such an embodiment, obtaining a training data set includes assigning a plurality of class labels to the corresponding plurality of training row vector. In such an embodiment, obtaining a training data set includes create a group of one or more sets of training row vectors from the plurality of training row vectors based on the plurality of class labels.

In one embodiment, obtaining the transformation training matrix includes calculating posterior probability of each column of one or more sets of training row vectors corresponding to the plurality of class labels of the training data set. In such an embodiment, obtaining the transformation training matrix includes obtaining an image feature pattern vector based on calculated posterior probability, wherein the image feature pattern vector is corresponding to relation of features of the image with class labels. In such an embodiment, obtaining the transformation training matrix includes performing an incremental clustering technique on the image feature pattern vector based on the similarity function to obtain individual clusters. In such an embodiment, obtaining the transformation training matrix includes generating an individual cluster mean and deviation for each column of the image feature pattern vector based on one or more input parameters, wherein one or more input parameter comprises at least one of a similarity value, a standard deviation or a combination thereof. In such an embodiment, obtaining the transformation training matrix includes performing the incremental clustering technique on the image feature pattern vector based on the individual cluster mean and deviation to obtain similarity value corresponding to each element of the image feature pattern vector. In such an embodiment, obtaining the transformation training matrix includes generating membership matrix based on the image feature pattern vector and similarity value. In such an embodiment, obtaining the transformation training matrix includes converting the membership matrix into binary form matrix by comparing similarity value of each column of the membership matrix with a threshold similarity value, wherein the binary form matrix is corresponding to the transformation training matrix.

The method further includes identifying, by a similarity identification module, a similarity value of each row vector of the dimensionality reduced test matrix obtained from the dimensionality reduction module with a dimensionality reduced training matrix obtained from the transformation training matrix using a similarity function in step 460. In a specific embodiment, the dimensionality reduction training matrix is obtained based on the training data set and the transformation training matrix. In another embodiment, the similarity identification module is configured to identify a similarity value of each row vector of the dimensionality reduced test matrix with a dimensionality reduced training matrix based on each individual class label.

The method further includes assigning, by a disease classification module, a class label of a training row vector to the test row vector based on a maximum similarity value obtained from the similarity identification module in step 470. The method further includes identifying, by the disease classification module, a type of disease present in the image of diseased area of a human body based on the class label classified by the disease classification module in step 480. In one embodiment, the class label may include a set of images from the plurality of training images corresponding to a type of disease. Various embodiments of the system and method for diagnosis of diseases from medical images as described above enable system to diagnose one or more diseases, especially a corona virus infection with high precision, temperature -pulse and respiration (TPR), trap- neuter return (TNR), less false positive rates (FPR) and less false negative rates (FNR). Further, the system is based on image feature transformation, which helps to achieve optimal image feature dimensionally by projecting images to new transformation space and improvement in performance of classifier accuracy and other evaluation parameters, such as better precision, specificity, sensitivity, and the like using an artificial intelligence learning model and a deep learning model. Further, the system provides diagnoses results in a short span of time, which makes the system very time effective.

The system provides an approach of Image feature transformation helps to achieve optimal image feature dimensionality by projecting images to new transformation space. This would yield improvement in performance of classifier accuracy and other evaluation parameters. Such approach is based on projecting images to new transformation space such as gaussian and z-space by application of gaussian or z-score similarity functions.

The system involves processing and analysis of images of different resolution in real-time, for providing classified disease which is a statistical combination and correlation of different resolution of images. Therefore, the system provides the flexibility of combining and correlating any number of images of different resolution. Such combination of the images of different resolution provides better, accurate and in-depth visibility of even minute structures within the images.

Moreover, the method performed by the system is not a preset execution program, rather the system performs real-time analysis using machine learning techniques, which may provide different results based on processing of different images and not a preset output. The processor of the system comprises various modules which enables the system to function in the aforementioned manner to determine a value of the feature vector by combining features of various modes of images. Each of the one or more modules of the system can be implemented as hardware. It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the disclosure and are not intended to be restrictive thereof.

While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person skilled in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.

The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, the order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.