Description:BACKGROUND
Field of the invention
[001] Embodiments of the present invention generally relate to a system for detection of disease in users and particularly to a system and method for detection of coronavirus in users.
Description of Related Art
[002] SARS-CoV-2 virus is an infectious disease that causes Coronavirus illness. Majority of those who are infected with the virus will have mild to moderate respiratory symptoms and will recover without a need for medical attention. Some, on the other hand, gets critically unwell and requires medical assistance. Serious sickness is more likely to strike elder persons and those with underlying medical disorders such as cardiovascular disease, diabetes, chronic respiratory disease, or cancer. COVID-19 can make anyone sick and cause them to get ill or die at any age. Being thoroughly informed on the disease and how it spreads is the greatest strategy to avoid. Staying at least 1 meter apart from people, wearing a well-fitted mask, and washing hands or using an alcohol-based rub periodically are all ways to protect ourselves and others against illness. When an infected person coughs, sneezes speak, sings, or breathes, the virus spreads in microscopic liquid particles from their mouth or nose. It's crucial to follow appropriate respiratory etiquette, such as coughing into a flexed elbow and to stay at home and self-isolate until the person feels better.
[003] Furthermore, the COVID-19 pandemic has resulted in a significant loss of human life throughout a world and poses an unprecedented threat to public health, food systems, and workplace. The pandemic's economic and social effects are devastating: tons of millions of people are at risk of sliding into severe poverty, and the number of people who are undernourished is on the rise.
[004] There is thus a need for a system that can detect the coronavirus in users in an efficient manner.
SUMMARY
[005] Embodiments in accordance with the present invention provide a system for detection of coronavirus in users. The system includes a processor located on an application server. The system further includes a storage medium comprising programming instructions executable by the processor. The storage medium includes an image receiving module configured to receive a medical image from a user device. The storage medium further includes a feature extraction module configured to extract features from the received medical image based on a training image set by using deep learning training models. The storage medium further includes a coronavirus detection module configured to detect the coronavirus in the users by correlating the extracted features of the received medical image with a dataset of pre-stored medical images with various coronavirus symptoms. The storage medium further includes a classification module configured to classify the received medical image based on the extracted features into a coronavirus positive image or a coronavirus negative image. The storage medium further includes a storage module configured to store the classified medical image into one of, a coronavirus positive directory or a coronavirus negative directory.
[006] Embodiments in accordance with the present invention further provide a method for detection of coronavirus in users. The method comprising steps of: receiving medical image from a user device; extracting features from the received medical image; detecting a coronavirus by correlating the received medical image with a dataset of pre-stored medical images; classifying the received medical image based on the extracted features into a coronavirus positive image or a coronavirus negative image; and storing the classified medical image into one of, a coronavirus positive directory or a coronavirus negative directory.
[007] Embodiments of the present invention may provide a number of advantages depending on its particular configuration. First, embodiments of the present application may provide a system for detection of coronavirus in users.
[008] Next, embodiments of the present application may provide a system for detection of coronavirus in users that is accurate and precise in detection.
[009] Next, embodiments of the present application may provide a system for detection of coronavirus in users that is cost-effective and user friendly.
[0010] These and other advantages will be apparent from the present application of the embodiments described herein.
[0011] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] The above and still further features and advantages of embodiments of the present invention will become apparent upon consideration of the following detailed description of embodiments thereof, especially when taken in conjunction with the accompanying drawings, and wherein:
[0013] FIG. 1 illustrates a block diagram depicting a system for detection of coronavirus in users, according to an embodiment of the present invention;
[0014] FIG. 2A illustrates a graph depicting an outcome from a Visual Geometry Group with layer 19 (VGG-19) deep learning model, according to an embodiment of the present invention;
[0015] FIG. 2B illustrates a classification report from the Visual Geometry Group with layer 19 (VGG-19) deep learning model, according to an embodiment of the present invention;
[0016] FIG. 3A illustrates a graph depicting an outcome from a Residual Neural Network (ResNet-50) deep learning model, according to an embodiment of the present invention;
[0017] FIG. 3B illustrates a classification report from the Residual Neural Network (ResNet-50) deep learning model, according to an embodiment of the present invention;
[0018] FIG. 4A illustrates a graph depicting an outcome from an Inception Version 3 deep learning model, according to an embodiment of the present invention;
[0019] FIG. 4B illustrates a classification report from the Inception Version 3 deep learning model, according to an embodiment of the present invention;
[0020] FIG. 5A illustrates a graph depicting an outcome from an Xception deep learning model, according to an embodiment of the present invention;
[0021] FIG. 5B illustrates a classification report from the Xception deep learning model, according to an embodiment of the present invention; and
[0022] FIG. 6 depicts a flowchart of a method for detection of the coronavirus in the users, according to an embodiment of the present invention.
[0023] The headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description or the claims. As used throughout this application, the word "may" is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include”, “including”, and “includes” mean including but not limited to. To facilitate understanding, like reference numerals have been used, where possible, to designate like elements common to the figures. Optional portions of the figures may be illustrated using dashed or dotted lines, unless the context of usage indicates otherwise.
DETAILED DESCRIPTION
[0024] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the spirit and scope of the invention as defined in the claims.
[0025] In any embodiment described herein, the open-ended terms "comprising", "comprises”, and the like (which are synonymous with "including", "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of", “consists essentially of", and the like or the respective closed phrases "consisting of", "consists of”, the like.
[0026] As used herein, the singular forms “a”, “an”, and “the” designate both the singular and the plural, unless expressly stated to designate the singular only.
[0027] FIG. 1 illustrates a block diagram depicting a system 100 for detection of coronavirus in users (hereinafter referred to as the system 100), according to an embodiment of the present invention. In an embodiment of the present invention, the system 100 may detect the coronavirus in the user by scanning a medical image provided by the user. According to embodiments of the present invention, the medical image may be, but not limited to, a Magnetic Resonance Imaging (MRI), a sonography, and so forth. In a preferred embodiment of the present invention, the medical image may be of a Computed Tomography (CT) Scan or an X-Ray scan. Embodiments of the present invention are intended to include or otherwise cover any type of the medical image, including known, related art, and/or later developed technologies. According to embodiments of the present invention, the user may be of any age group and any gender such as, but not limited to, a child, an adolescent, an adult, an old age, and so forth. Embodiments of the present invention are intended to include or otherwise cover any age group of the user.
[0028] According to an embodiment of the present invention, the system 100 may comprise a user device 102, a Flask application 104, a database 106, a dataset 108, an application server 110, a processor 112, and a storage medium 114.
[0029] In an embodiment of the present invention, the user device 102 may be a device used by the user to upload medical image into the system 100. In a preferred embodiment of the present invention, the medical image may have a resolution of 224 pixels by 224 pixels.
[0030] The user device 102 may be, but not limited to, a personal computer, a consumer device, and alike. Embodiments of the present invention are intended to include or otherwise cover any type of the user device 102 including known, related art, and/or later developed technologies. In an embodiment of the present invention, the personal computer may be, but not limited to, a desktop, a server, a laptop, and alike. Embodiments of the present invention are intended to include or otherwise cover any type of the personal computer including known, related art, and/or later developed technologies.
[0031] Further, in an embodiment of the present invention, the consumer device may be, but not limited to, a tablet, a mobile phone, a notebook, a netbook, a smartphone, a wearable device, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the consumer device including known, related art, and/or later developed technologies. Embodiments of the present invention are intended to include or otherwise cover any type of the user device 102 including known, related art, and/or later developed technologies.
[0032] According to an embodiment of the present invention, the user device 102 may comprise software applications such as, but not limited to, a healthcare application, a medical consultation application, an emergency services application, and the like. In a preferred embodiment of the present invention, the user device 102 may comprise the Flask application 104 that may be a computer-readable program installed in the user device 102 for executing functions through associated with the system 100.
[0033] In an embodiment of the present invention, the database 106 may store the dataset 108. The dataset 108 may further comprise medical images that may be pre-stored with various coronavirus symptoms in the database 106, in an embodiment of the present invention. In an embodiment of the present invention, the dataset 108 may store 1000 medical images. According to embodiments of the present invention, the database 106 may be for example, but not limited to, a distributed database, a personal database, an end-user database, a commercial database, a Structured Query Language (SQL) database, a non-SQL database, an operational database, a relational database, an object-oriented database, a graph database, a cloud server database, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the database 106 including known, related art, and/or later developed technologies.
[0034] Further, the database 106 may be stored in a cloud server, in an embodiment of the present invention. In an embodiment of the present invention, the cloud server may be remotely located. In an exemplary embodiment of the present invention, the cloud server may be a public cloud server. In another exemplary embodiment of the present invention, the cloud server may be a private cloud server. In yet another embodiment of the present invention, the cloud server may be a dedicated cloud server. According to embodiments of the present invention, the cloud server may be, but not limited to, a Microsoft Azure cloud server, an Amazon AWS cloud server, a Google Compute Engine (GEC) cloud server, an Amazon Elastic Compute Cloud (EC2) cloud server, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the cloud server including known, related art, and/or later developed technologies.
[0035] In an embodiment of the present invention, the application server 110 may be a hardware on which the processor 112 may be installed. According to embodiments of the present invention, the application server 110 may be, but not limited to, a motherboard, a wired board, a mainframe, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the application server 110, including known, related art, and/or later developed technologies.
[0036] In an embodiment of the present invention, the processor 112 may be located on the application server 110. The processor 112 may be configured to execute the computer-readable instructions to generate an output relating to the system 100. According to embodiments of the present invention, the processor 112 may be, but not limited to, a Programmable Logic Control (PLC) unit, a microprocessor, a development board, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the processor 112 including known, related art, and/or later developed technologies.
[0037] In an embodiment of the present invention, the storage medium 114 may store computer programmable instructions in form of programming modules. The storage medium 114 may be a non-transitory storage medium, in an embodiment of the present invention. In an embodiment of the present invention, the storage medium 114 may store the medical image uploaded by the user through the Flask application 104 installed in the user device 102. The storage medium 114 may communicate with the processor 112 and execute computer readable set of instructions present in storage medium 114, in an embodiment of the present invention.
[0038] According to embodiments of the present invention, the storage medium 114 may be, but not limited to, a Random-Access Memory (RAM), a Static Random-access Memory (SRAM), a Dynamic Random-Access Memory (DRAM), a Read Only Memory (ROM), an Erasable Programmable Read-only Memory (EPROM), an Electrically Erasable Programmable Read-only Memory (EEPROM), a NAND Flash, a Secure Digital (SD) memory, a cache memory, a Hard Disk Drive (HDD), a Solid-State Drive (SSD) and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the storage medium 114, including known, related art, and/or later developed technologies. In an embodiment of the present invention, the storage medium 114 may further comprise an image receiving module 116, a feature extraction module 118, a coronavirus detection module 120, a classification module 122, and a storage module 124.
[0039] In an embodiment of the present invention, the image receiving module 116 may be configured to receive the medical image from the user device 102. The image receiving module 116 may further transmit the received image to the feature extraction module 118, in an embodiment of the present invention.
[0040] In an embodiment of the present invention, the feature extraction module 118 may be configured to extract features from the received medical image. The features may be extracted by using a deep learning training model, in an embodiment of the present invention. In an embodiment of the present invention, the deep learning training models may refer to a feature extracting network which is used within the Deep Lab architecture. This feature extractor may be used to encode the user input into certain feature representation, in an embodiment of the present invention. According to embodiments of the present invention, the deep learning training models may be, but not limited to, a Visual Geometry Group with layer 19 (VGG-19), a Residual Neural Network (ResNet-50), an Inception Version 3, an Xception, and so forth. Embodiments of the present invention are intended to include or otherwise cover any deep learning training models, including known, related art, and/or later developed technologies. In an embodiment of the present invention, the features extracted by the feature extraction module 118 may further be transmitted to the coronavirus detection module 120.
[0041] In an embodiment of the present invention, the coronavirus detection module 120 may be configured to detect the coronavirus in the users by correlating the extracted features of the received medical image. The extracted features may be correlated with the dataset 108 of the pre-stored medical images with various coronavirus symptoms, in an embodiment of the present invention.
[0042] In an embodiment of the present invention, the classification module 122 may be configured to classify the received medical image based on the extracted features into a coronavirus positive image or a coronavirus negative image.
[0043] In an embodiment of the present invention, the storage module 124 may be configured to store the classified medical image into one of, a coronavirus positive directory or a coronavirus negative directory. The coronavirus positive directory may store the classified medical image that may be detected with coronavirus infection, in an embodiment of the present invention. In an embodiment of the present invention, the coronavirus negative directory may store the classified medical image that may not be detected with coronavirus infection.
[0044] FIG. 2A illustrates a graph 200 depicting an outcome from Visual Geometry Group with layer 19 (VGG-19) deep learning model, according to an embodiment of the present invention. In an embodiment of the present invention, the graph 200 may represent an epoch versus accuracy graph that may refer to an accuracy of the Visual Geometry Group with layer 19 (VGG-19) deep learning model. The Visual Geometry Group with layer 19 (VGG-19) deep learning model may belong to a neural network comprising of 19 distinct layers. The Visual Geometry Group with layer 19 (VGG-19) deep learning model may employ a possible set of initially trained weights generated through “ImageNet dataset” which comprises of more than 14 million sampled images that fit into 1000 distinct classes. The medical image provided to the Visual Geometry Group with layer 19 (VGG-19) deep learning model may be of 224 pixels by 224 pixels.
[0045] FIG. 2B illustrates a classification report 202 from the Visual Geometry Group with the layer 19 (VGG-19) deep learning model, according to an embodiment of the present invention. In an embodiment of the present invention, the classification report 202 may be obtained using the medical image that may be supplied by the user from the user device 102 through the Flask application 104. The classification report 202 may include computed values such as, but not limited to, a precision value, a recall value, a f1-score value, a support value, and so forth. Embodiments of the present invention are intended to include or otherwise cover any computed value that may be included in the classification report 202. In an embodiment of the present invention, the classification report 202 may further include a coronavirus prediction result.
[0046] FIG. 3A illustrates a graph 300 depicting an outcome from the Residual Neural Network (ResNet-50) deep learning model, according to an embodiment of the present invention. In an embodiment of the present invention, the graph 300 may represent an epoch versus accuracy graph that may refer to the accuracy of the Residual Neural Network (ResNet-50) deep learning model. The Residual Neural Network (ResNet-50) deep learning model may be a 50 layered deep convolutional neural network that may utilize “ImageNet database” for performing import of an initially trained version of the proposed network. The Residual Neural Network (ResNet-50) deep learning model may be implemented by training over a million of photos or images as the network can be at the max sort photos in terms of 1000 distinct groupings. The medical image provided to the Residual Neural Network (ResNet-50) deep learning model may be of 224 pixels by 224 pixels.
[0047] FIG. 3B illustrates a classification report 302 from the Residual Neural Network (ResNet-50) deep learning model, according to an embodiment of the present invention. In an embodiment of the present invention, the classification report 302 may be obtained using the medical imaged that may be supplied by the user from the user device 102 through the Flask application 104. The classification report 302 may include computed values such as, but not limited to, the precision value, the recall value, the f1-score value, the support value and so forth. Embodiments of the present invention are intended to include or otherwise cover any computed values that may be included in the classification report 302. In an embodiment of the present invention, the classification report 302 may further include the coronavirus prediction result.
[0048] FIG. 4A illustrates a graph 400 depicting an outcome from the Inception Version 3 deep learning model, according to an embodiment of the present invention. In an embodiment of the present invention, the graph 400 may be the epoch versus accuracy graph that may refer to the accuracy of the Inception Version 3 deep learning model. The Inception Version 3 deep learning model may be a 48 layered deep convolutional neural network tha may utilize “ImageNet database” for performing import of an initially trained version of the proposed network. The Inception Version 3 deep learning model may be implemented by training over a million of photos or images as the network can be at the max sort photos in terms of 1000 distinct categories. The medical image provided to the Inception Version 3 deep learning model may be of 299 pixels by 299 pixels.
[0049] FIG. 4B illustrates a classification report 402 from the Inception Version 3 deep learning model, according to an embodiment of the present invention. In an embodiment of the present invention, the classification report 402 may be obtained using the medical image that may be supplied by the user from the user device 102 through the Flask application 104. The classification report 402 may include computed values such as, but not limited to, the precision value, the recall value, the f1-score value, the support value and so forth. Embodiments of the present invention are intended to include or otherwise cover any computed values that may be included in the classification report 402. In an embodiment of the present invention, the classification report 402 may further include the coronavirus prediction result.
[0050] FIG. 5A illustrates a graph 500 depicting the outcome from the Xception deep learning model, according to an embodiment of the present invention. In an embodiment of the present invention, the graph 500 may be an epoch versus accuracy graph that may refer to the accuracy of the Xception deep learning model. The Xception deep learning model may be an expansion of inception architecture that substitutes various tasks implemented using conventional modules with depth wise separable convolutions in inception. The Xception deep learning model may be a 71 layered deep convolutional neural network that may utilize “ImageNet database” for performing import of an initially trained version of the proposed network. The Xception deep learning model may be implemented by training over a million of photos or images as the network can be at the max sort photos in terms of 1000 distinct categories. The medical image provided to the Xception deep learning model may be of 299 pixels by 299 pixels.
[0051] FIG. 5B illustrates a classification report 502 from the Xception deep learning model, according to an embodiment of the present invention. In an embodiment of the present invention, the classification report 502 may be obtained using the medical image that may be supplied by the user from the user device 102 through the Flask application 104. The classification report 502 may include computed values such as, but not limited to, the precision value, the recall value, the f1-score value, the support value and so forth. Embodiments of the present invention are intended to include or otherwise cover any computed values that may be included in the classification report 502. In an embodiment of the present invention, the classification report 502 may further include the coronavirus prediction result.
[0052] FIG. 6 depicts a flowchart of a method 600 for detection of the coronavirus in the users, according to an embodiment of the present invention.
[0053] At step 602, the system 100 may receive the medical image from the user device 102.
[0054] At step 604, the system 100 may extract features from the received medical image.
[0055] At step 606, the system 100 may detect the coronavirus by correlating the received medical image with the dataset 108 of the pre-stored medical images.
[0056] At step 608, the system 100 may classify the received medical image based on the extracted features into the coronavirus positive image or the coronavirus negative image.
[0057] At step 610, the system 100 may store the classified medical image into one of, the coronavirus positive directory or the coronavirus negative directory.
[0058] Embodiments of the invention are described above with reference to block diagrams and schematic illustrations of methods and systems according to embodiments of the invention. It will be understood that each block of the diagrams and combinations of blocks in the diagrams can be implemented by computer program instructions. These computer program instructions may be loaded onto one or more general purpose computers, special purpose computers, or other programmable data processing apparatus to produce machines, such that the instructions which execute on the computers or other programmable data processing apparatus create means for implementing the functions specified in the block or blocks. Such computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means that implement the function specified in the block or blocks.
[0059] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it is to be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
[0060] This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope the invention is defined in the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements within substantial differences from the literal languages of the claims.
, Claims:I/We Claim:
1. A system (100) for detection of coronavirus in users, the system (100) comprising:
a processor (112) located on an application server (110); and
a storage medium (114) comprising programming instructions executable by the processor (112), wherein the storage medium (114) comprises:
an image receiving module (116) configured to receive a medical image from a user device (102);
a feature extraction module (118) configured to extract features from the received medical image based on a training image set by using deep learning training models;
a coronavirus detection module (120) configured to detect the coronavirus in the users by correlating the extracted features of the received medical image with a dataset (108) of pre-stored medical images with various coronavirus symptoms;
a classification module (122) configured to classify the received medical image based on the extracted features into a coronavirus positive image or a coronavirus negative image; and
a storage module (124) configured to store the classified medical image into one of, a coronavirus positive directory or a coronavirus negative directory.
2. The system (100) as claimed in claim 1, wherein the medical image is selected from one of, a Computed Tomography (CT) scan image or an X-Ray image.
3. The system (100) as claimed in claim 1, comprising a Flask application (104) installed on the user device (102).
4. The system (100) as claimed in claim 1, wherein the deep learning training models are selected from a Visual Geometry Group with layer 19 (VGG-19), a Residual Neural Network (ResNet-50), an Inception Version 3, an Xception, or a combination thereof.
5. The system (100) as claimed in claim 1, wherein the medical image is having a resolution of 224 pixels by 224 pixels.
6. A method (600) for detection of coronavirus in users, the method (600) comprising steps of:
receiving a medical image from a user device (102);
extracting features from the received medical image;
detecting a coronavirus by correlating the received medical image with a dataset (108) of pre-stored medical images;
classifying the received medical image based on the extracted features into a coronavirus positive image or a coronavirus negative image; and
storing the classified medical image into one of, a coronavirus positive directory or a coronavirus negative directory.
7. The method (600) as claimed in claim 6, wherein the medical image is selected from one of, a Computed Tomography (CT) scan image or an X-Ray image.
8. The method (600) as claimed in claim 6, comprising a Flask application (104) installed on the user device (102).
9. The method (600) as claimed in claim 6, wherein the deep learning training models are selected from a Visual Geometry Group with layer 19 (VGG-19), a Residual Neural Network (ResNet-50), an Inception Version 3, Xception, or a combination thereof.
10. The method (600) as claimed in claim 6, wherein the medical image is having a resolution of 224 pixels by 224 pixels.
Date: 09th May 2022
Place: Noida

Nainsi Rastogi
Patent Agent (IN/PA-2372)
Agent for the Applicant