AUTOMATED DIABETIC RETINOPATHY DETECTION SYSTEM USING DEEP LEARNING

Abstract

ABSTRACT “AUTOMATED DIABETIC RETINOPATHY DETECTION SYSTEM USING DEEP LEARNING” The present invention provides automated diabetic retinopathy detection system using deep learning that deals with the requirement of the proper, efficient, and accessible detection of Diabetic Retinopathy (DR), which is one of the major causes of blindness in diabetic patients. Traditional methods of diagnosing DR depend on skilled ophthalmologists who manually scan retinal images, thus being time-consuming, subjective, and dependent on medical experts' availability. With limited healthcare resources, in such regions, diagnosis will be delayed, and patients will lose their sight irreversibly because DR will continue to progress. Figure 1

Specification

Description:TECHNICAL FIELD
[0001] The present invention relates to the field of automated health monitoring systems, and more particularly, the present invention relates to the automated diabetic retinopathy detection system using deep learning.
BACKGROUND ART
[0002] The following discussion of the background of the invention is intended to facilitate an understanding of the present invention. However, it should be appreciated that the discussion is not an acknowledgment or admission that any of the material referred to was published, known, or part of the common general knowledge in any jurisdiction as of the application's priority date. The details provided herein the background if belongs to any publication is taken only as a reference for describing the problems, in general terminologies or principles or both of science and technology in the associated prior art.
[0003] The proposed solution is an Automated Diabetic Retinopathy Detection System that is based on a deep learning model, DR NET V2, with the analysis of retinal images to identify stages of diabetic retinopathy. It integrates with digital retinal imaging devices to provide health care providers with a reliable and efficient diagnostic tool, reducing the need for special ophthalmologists.
[0004] Traditional and less sophisticated approaches to diabetic retinopathy screening include:
[0005] Fundus Photography: A very old technique in which, with a special camera designed for photography of the retina, photographs are clicked and read by trained eye doctors. It is relatively subjective and time-consuming with a required expertise in visual image interpretation.
[0006] Optical Coherence Tomography: This is a form of non-invasive imaging that will provide cross-sectional images of the retina, commonly used in assessing retinal diseases. It however, is expensive and may not be available in all settings, and the interpretation requires trained personnel.
[0007] Visual Acuity Test: Routine eye examinations diagnose the presence of the ability to see a certain distance, signifying severe DR, but do not make a precise diagnosis of retinal condition. This measure detects only the advanced stages and fails to identify early change.
[0008] Fluorescein Angiography: This is an invasive procedure where a fluorescent dye is injected into the blood and photographs are taken of the retina to note any abnormalities. It necessitates specialized equipment and personnel and carries a risk of allergic reactions to the dye.
[0009] Screening Guidelines and Manual Chart Reviews: Most health systems depend on the time-interval-based clinical practice guidelines together with manual review of the charts to note down those patients who risk DR. This methodological approach fails to diagnose them since relying on time-lapse intervals, missed possibilities may go unnoticed in being important.
[0010] Although traditional methods such as these have been effective enough for the diagnosis of diabetic retinopathy, they still lack efficiency due to subjectivity, the availability of trained personnel, and the need for advanced technology.
[0011] In light of the foregoing, there is a need for Automated diabetic retinopathy detection system using deep learning that overcomes problems prevalent in the prior art associated with the traditionally available method or system, of the above-mentioned inventions that can be used with the presented disclosed technique with or without modification.
[0012] All publications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies, and the definition of that term in the reference does not apply.
OBJECTS OF THE INVENTION
[0013] The principal object of the present invention is to overcome the disadvantages of the prior art by providing automated diabetic retinopathy detection system using deep learning.
[0014] Another object of the present invention is to provide automated diabetic retinopathy detection system using deep learning that deals with the requirement of the proper, efficient, and accessible detection of Diabetic Retinopathy (DR), which is one of the major causes of blindness in diabetic patients.
[0015] Another object of the present invention is to provide automated diabetic retinopathy detection system using deep learning that is free from these limitations and utilizes a deep learning model called DR NET V2 to provide reliable, automated analysis of retinal images to accurately detect and classify DR stages.
[0016] Another object of the present invention is to provide automated diabetic retinopathy detection system using deep learning that speeds up the process of diagnosis, increases early detection, and minimizes dependence on special manpower, making this innovation within reach for wider implementation in health systems across the globe.
[0017] The foregoing and other objects of the present invention will become readily apparent upon further review of the following detailed description of the embodiments as illustrated in the accompanying drawings.
SUMMARY OF THE INVENTION
[0018] The present invention relates to automated diabetic retinopathy detection system using deep learning that invention deals with the requirement of the proper, efficient, and accessible detection of Diabetic Retinopathy (DR), which is one of the major causes of blindness in diabetic patients. Traditional methods of diagnosing DR depend on skilled ophthalmologists who manually scan retinal images, thus being time-consuming, subjective, and dependent on medical experts' availability. With limited healthcare resources, in such regions, diagnosis will be delayed, and patients will lose their sight irreversibly because DR will continue to progress.
[0019] The available automated solutions lack precision in the identification of various DR stages or are incapable of being integrated seamlessly with clinical workflows, which severely limits their practical application. The present invention is free from these limitations and utilizes a deep learning model called DR NET V2 to provide reliable, automated analysis of retinal images to accurately detect and classify DR stages. It speeds up the process of diagnosis, increases early detection, and minimizes dependence on special manpower, making this innovation within reach for wider implementation in health systems across the globe.
[0020] 1. Digital Retinopathy System (DRS): It is an imaging device that takes a very high-resolution picture of a patient's retina and the images are then processed by a computer system where the device is attached.
[0021] 2. Computer Setup with DR Detection Software It equips the computer system with the DR detection software that runs on a specially developed CNN model known as DR NET V2. The system does pre-processing on the given retinal images through use of filters such as Gaussian, HSB, YCbCr, and Laplacian to enhance critical features which are then fed into the model.
[0022] 3. DR NET V2 Deep Learning Model: This model is able to carry out very precise classification of the DR stages. Convolutional, pooling, and dense layers as given in the layer diagram are used for this processing by the retinal images with a prediction regarding the DR stage. These predictions provide a stage value for the predicted DR stage, and in addition, corresponding confidence scores for better understanding of results.
[0023] 4. Prediction Output: This generates output prediction that displays the predicted DR stage with the original retinal image, thus helping the healthcare provider know the outcome in a moment. It can be interoperable with EHR that makes patient management easier.
[0024] While the invention has been described and shown with reference to the preferred embodiment, it will be apparent that variations might be possible that would fall within the scope of the present invention.
BRIEF DESCRIPTION OF DRAWINGS
[0025] So that the manner in which the above-recited features of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may have been referred by embodiments, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only typical embodiments of this invention and are therefore not to be considered limiting of its scope, for the invention may admit to other equally effective embodiments.
[0026] These and other features, benefits, and advantages of the present invention will become apparent by reference to the following text figure, with like reference numbers referring to like structures across the views, wherein:
[0027] Figure 1 shows a automated diabetic retinopathy detection system using deep learning, in accordance with an exemplary embodiment of the present invention;
[0028] Figure 2: Layer Structure for DR NET V2
DETAILED DESCRIPTION OF THE INVENTION
[0029] While the present invention is described herein by way of example using embodiments and illustrative drawings, those skilled in the art will recognize that the invention is not limited to the embodiments of drawing or drawings described and are not intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and the detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claim.
[0030] As used throughout this description, 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). Further, the words "a" or "an" mean "at least one" and the word "plurality" means "one or more" unless otherwise mentioned. Furthermore, the terminology and phraseology used herein are solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and additional subject matter not recited, and is not intended to exclude other additives, components, integers, or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles, and the like are included in the specification solely for the purpose of providing a context for the present invention. It is not suggested or represented that any or all these matters form part of the prior art base or were common general knowledge in the field relevant to the present invention.
[0031] In this disclosure, whenever a composition or an element or a group of elements is preceded with the transitional phrase "comprising", it is understood that we also contemplate the same composition, element, or group of elements with transitional phrases "consisting of", "consisting", "selected from the group of consisting of, "including", or "is" preceding the recitation of the composition, element or group of elements and vice versa.
[0032] The present invention is described hereinafter by various embodiments with reference to the accompanying drawing, wherein reference numerals used in the accompanying drawing correspond to the like elements throughout the description. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiment set forth herein. Rather, the embodiment is provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those skilled in the art. In the following detailed description, numeric values and ranges are provided for various aspects of the implementations described. These values and ranges are to be treated as examples only and are not intended to limit the scope of the claims. In addition, several materials are identified as suitable for various facets of the implementations. These materials are to be treated as exemplary and are not intended to limit the scope of the invention.
[0033] The present invention relates to automated diabetic retinopathy detection system using deep learning that invention deals with the requirement of the proper, efficient, and accessible detection of Diabetic Retinopathy (DR), which is one of the major causes of blindness in diabetic patients. Traditional methods of diagnosing DR depend on skilled ophthalmologists who manually scan retinal images, thus being time-consuming, subjective, and dependent on medical experts' availability. With limited healthcare resources, in such regions, diagnosis will be delayed, and patients will lose their sight irreversibly because DR will continue to progress.
[0034] The available automated solutions lack precision in the identification of various DR stages or are incapable of being integrated seamlessly with clinical workflows, which severely limits their practical application. The present invention is free from these limitations and utilizes a deep learning model called DR NET V2 to provide reliable, automated analysis of retinal images to accurately detect and classify DR stages. It speeds up the process of diagnosis, increases early detection, and minimizes dependence on special manpower, making this innovation within reach for wider implementation in health systems across the globe.
[0035] 1. Digital Retinopathy System (DRS): It is an imaging device that takes a very high-resolution picture of a patient's retina and the images are then processed by a computer system where the device is attached.
[0036] 2. Computer Setup with DR Detection Software It equips the computer system with the DR detection software that runs on a specially developed CNN model known as DR NET V2. The system does pre-processing on the given retinal images through use of filters such as Gaussian, HSB, YCbCr, and Laplacian to enhance critical features which are then fed into the model.
[0037] 3. DR NET V2 Deep Learning Model: This model is able to carry out very precise classification of the DR stages. Convolutional, pooling, and dense layers as given in the layer diagram are used for this processing by the retinal images with a prediction regarding the DR stage. These predictions provide a stage value for the predicted DR stage, and in addition, corresponding confidence scores for better understanding of results.
[0038] 4. Prediction Output: This generates output prediction that displays the predicted DR stage with the original retinal image, thus helping the healthcare provider know the outcome in a moment. It can be interoperable with EHR that makes patient management easier.
Description of Workflow
[0039] Image Capture: The patient's retina is imaged using the Digital Retinopathy System.
[0040] Image Processing and Analysis: The image thus processed through the computerized DR NET V2 feature enhancement filters is analyzed.
[0041] Prediction and Output: The model determines the DR stage, decides classification, and projects the predictability to the physician such that he or she immediately takes a decision.
[0042] DR NET V2 is a custom convolutional neural network designed with the Fastai library for diabetic retinopathy image classification. The architecture begins with an input layer, followed by a series of convolutional layers that gradually extract features from images, starting with 64 channels and scaling up to 512 channels for detailed feature extraction. For the stabilization of each convolutional layer with efficient gradient flow, we use Batch Normalization and ReLU activation functions; further, for residual learning to train more deeply, BasicBlocks are used. Reducing the spatial dimensions and retaining significant details is made possible using Max Pooling and Adaptive Pooling layers, resulting in AdaptiveConcatPool2d, which is an accumulation of features from global average and max pooling. These are then flattened and fed into fully connected layers; the final dense layer using softmax activation function predicts over five classes. Therefore, with 21,814,592 parameters the model is deep and complicated enough to successfully capture feature for accurate classification. Fig.2 illustrates this detailed layer structure in visualization.
[0043] This system enables fast and accurate DR screening, particularly useful in regions with limited access to specialist eye care, thus improving early detection and patient outcomes. For greater technical clarity, a detailed view of the architecture is provided in the neural network layer diagram.
[0044] This Automated Diabetic Retinopathy Detection System is endowed with the following sophisticated features: innovative features that enhance the sophistication of the system, which include:
[0045] Custom Deep Learning Model (DR NET V2): The system is using a custom CNN model, highly optimized for the detection of diabetic retinopathy. It achieves high accuracy in DR stage identification and classification.
[0046] Advanced Feature Extraction: Application of filters like Gaussian, HSB, YCbCr, and Laplacian for the extraction of features enhances the retinal images so that the model can easily identify varied stages of DR.
[0047] Automated and Real-Time Analysis: It is suitable with the digital retinal imaging devices to offer real-time and automated analysis of retinal images. In this way, diagnostic time is reduced along with human skill.
[0048] Stage-Specific Classification: This model classifies the severity of DR into different stages, including no DR, mild, moderate, severe, and proliferative DR, so treatment decisions can be made at the specific stage detected.
[0049] Easy-to-Use System for Healthcare Integration: This system offers easy-to-interpret results, including the DR stage and confidence scores, which can be integrated with EHR to facilitate easy patient management.
[0050] Low-Cost, Scalable Solution for Resource-Limited Settings: The automated deep learning-based system depends less on advanced apparatus and specialized skills in making their decisions hence the solution will be a cheap solution which easily scales for use in under-resource clinics.
[0051] Compatibility with existing Digital Retinal Imaging Devices.
[0052] These features come together to make the invention an advanced, accessible, and effective tool for early diagnosis of diabetic retinopathy, improving patient outcomes and expanding access to retinal care.
[0053] Various modifications to these embodiments are apparent to those skilled in the art from the description and the accompanying drawings. The principles associated with the various embodiments described herein may be applied to other embodiments. Therefore, the description is not intended to be limited to the 5 embodiments shown along with the accompanying drawings but is to be providing the broadest scope consistent with the principles and the novel and inventive features disclosed or suggested herein. Accordingly, the invention is anticipated to hold on to all other such alternatives, modifications, and variations that fall within the scope of the present invention and appended claims.
, Claims:CLAIMS
We Claim:
1) An automated diabetic retinopathy detection system, the system comprising:
a Digital Retinopathy System (DRS) for capturing high-resolution retinal images;
a computer setup integrated with DR detection software that preprocesses retinal images through Gaussian, HSB, YCbCr, and Laplacian filters; and
a deep learning model, DR NET V2, that classifies diabetic retinopathy stages, wherein the system provides predictions with associated confidence scores to assist healthcare providers in diagnosis.
2) The system as claimed in claim 1, wherein the DR NET V2 model comprises a convolutional neural network architecture with a series of convolutional, pooling, and dense layers that extract and classify image features, utilizing Batch Normalization, ReLU activation functions, BasicBlocks for residual learning, and an AdaptiveConcatPool2d layer for feature aggregation.
3) The system as claimed in claim 1, wherein the DR NET V2 model classifies diabetic retinopathy into multiple stages, including no DR, mild, moderate, severe, and proliferative DR, allowing for stage-specific classification and facilitating timely clinical decision-making.
4) The system as claimed in claim 1, wherein the system further comprising a real-time, automated analysis feature that processes retinal images immediately after capture, allowing for rapid diagnosis and integration with electronic health records (EHR) for streamlined patient management.
5) The system as claimed in claim 1, wherein the DR NET V2 model is compatible with various digital retinal imaging devices, providing an accessible, low-cost, and scalable solution suitable for use in resource-limited clinical settings to enable widespread diabetic retinopathy screening.

Documents