Diabetic Retinopathy Detection using Retinal Images and Deep Learning Model (original) (raw)

DIABETIC RETINOPATHY DETECTION USING DEEP LEARNING

IAEME PUBLICATION, 2020

Diabetic Retinopathy (DR) is an ailment that arises in patients suffering from Diabetes for more than 2 decades. This causes blindness due to elevated blood glucose levels. The condition can be cured when it is diagnosed at an early stage. The currently available methods to diagnose Diabetic Retinopathy is Fluorescein Angiography, which is slow and is not available to masses. In this paper we propose a novel solution by incorporating Convolutional Neural Networks (CNN) to detect the presence of Diabetic Retinopathy using the color fundus image of the patient. We have built a Neural Network architecture consisting of two stages of CNN where the first stage detects the presence of DR and the second stage classifies it into four stages. With this architecture, we have achieved an accuracy of 90.32%. The dataset we used to train the Neural Network was taken from a Kaggle competition that consisted of 3,662 color fundus images.

Deep Learning-based Diabetic Retinopathy Detection for automated health assessment

Diabetic Retinopathy(DR) is a type of chronic microvascular diabetes complication that deteriorates the human vision system and leads to a patient towards complete blindness. DR is a common condition among people with diabetes, affecting an estimated 93 million people. Early detection of DR is a vital strategy to alleviate massive vision impairment. In this research, we have experienced DR patients and tried to determine whether they are DR affected or not. We present a lightweight and quick detection system for classifying DR over fundus images of two sets: healthy retina or DR-affected retina. After pre-processing, we produced the dataset annotations needed for model training. Next, we add the MobilenetV2 to Depthwise Separable Convolution (DSC) level to compute the representative collection of data. The proposed system is trained in a standard approach for further classification using a cross-entropy loss function. We have performed comprehensive testing on a dataset of 3662 fundus images from the Kaggle competition. The proposed model performs rapid detection with a high-level accuracy of 97.82%. It also obtained 95% sensitivity and 96% specificity on the test dataset. We have evaluated our method with the existing contemporary approaches to highlight its robustness based on DR classification. We have achieved incredible results over other traditional methods based on computational complexity and training time. The proposed early DR detection system will be a promising tool for improving the management and treatment of diabetic patients.

Detection of Diabetic Retinopathy Using Deep Learning

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

Diabetic retinopathy is one of the most dangerous complications of diabetes, leading to permanent blindness if left untreated. One of the major challenges is early detection, which is very important for the success of treatment. Unfortunately, accurate identification of the stage of diabetic retinopathy is notoriously tricky and requires expert human interpretation of fundus images. Simplifying the detection step is essential and can help millions of people. Convolutional Neural Networks (CNNs) have been successfully used in many neighboring subjects and for the diagnosis of diabetic retinopathy itself. However, the high cost of large annotated datasets as well as inconsistencies between different clinicians hinders the implementation of these methods. In this paper, we propose an automatic method based on deep learning to detect the stage of diabetic retinopathy using a single human fundus image. In addition, we propose a multi-stage transfer learning approach that uses similar datasets with different labels. The presented method can be used as a screening method for the early detection of diabetic retinopathy with a sensitivity and specificity of 0.99 and is ranked 54 out of 2943 competing methods (quadratic weighted kappa score 0.925466) on the APTOS 2019 Blindness Detection Dataset (13,000 images). I.

IJERT-Detection of Diabetic Retinopathy using Deep Learning

International Journal of Engineering Research and Technology (IJERT), 2021

https://www.ijert.org/detection-of-diabetic-retinopathy-using-deep-learning https://www.ijert.org/research/detection-of-diabetic-retinopathy-using-deep-learning-IJERTV10IS050333.pdf Diabetic Retinopathy (DR) is a common complication of diabetes mellitus, which causes lesions on the retina that effect vision. If it is not detected early, it can lead to blindness. Unfortunately, DR is not a reversible process, and treatment only sustains vision. DR early detection and treatment can significantly reduce the risk of vision loss. The manual diagnosis process of DR retina fundus images by ophthalmologists is time-, effort-, and cost-consuming and prone to misdiagnosis unlike computer-aided diagnosis systems. Recently, deep learning has become one of the most common techniques that has achieved better performance in many areas, especially in medical image analysis and classification. Convolutional neural networks are more widely used as a deep learning method in medical image analysis and they are highly effective. For this article, the recent state-ofthe-art methods of DR color fundus images detection and classification using deep learning techniques have been reviewed and analyzed. Furthermore, the DR available datasets for the color fundus retina have been reviewed. Difference challenging issues that require more investigation are also discussed. 1.

Diabetic Retinopathy Improved Detection Using Deep Learning

Applied Sciences

Diabetes is a disease that occurs when the body presents an uncontrolled level of glucose that is capable of damaging the retina, leading to permanent damage of the eyes or vision loss. When diabetes affects the eyes, it is known as diabetic retinopathy, which became a global medical problem among elderly people. The fundus oculi technique involves observing the eyeball to diagnose or check the pathology evolution. In this work, we implement a convolutional neural network model to process a fundus oculi image to recognize the eyeball structure and determine the presence of diabetic retinopathy. The model’s parameters are optimized using the transfer-learning methodology for mapping an image with the corresponding label. The model training and testing are performed with a dataset of medical fundus oculi images and a pathology severity scale present in the eyeball as labels. The severity scale separates the images into five classes, from a healthy eyeball to a proliferative diabetic r...

Design and development of a deep learning based application for detecting diabetic retinopathy

2019

Diabetic retinopathy (DR), a complication of diabetes, is one of the leading causes of blindness globally. Since early detection of DR can reduce the chance of vision loss significantly, regular retinal screening of diabetic patients is an essential prerequisite. However, due to inefficient manual detection as well as lack of resources and ophthalmologists, early detection of DR is severely hindered. Moreover, subtle differences among different severity levels and the presence of small anatomical components make the task of identification very challenging. The objective of this study is to develop a robust diagnostic system through integration of state-of-theart deep learning techniques for automated DR severity detection. We used the concept of deep Convolutional Neural Networks (CNNs), which have revolutionized different branches of computer vision including medical imaging. Our deep network is trained on the largest publicly available Kaggle data set using our very own novel loss...

Using Deep Learning Architectures for Detection and Classification of Diabetic Retinopathy

Sensors

Diabetic retinopathy (DR) is a common complication of long-term diabetes, affecting the human eye and potentially leading to permanent blindness. The early detection of DR is crucial for effective treatment, as symptoms often manifest in later stages. The manual grading of retinal images is time-consuming, prone to errors, and lacks patient-friendliness. In this study, we propose two deep learning (DL) architectures, a hybrid network combining VGG16 and XGBoost Classifier, and the DenseNet 121 network, for DR detection and classification. To evaluate the two DL models, we preprocessed a collection of retinal images obtained from the APTOS 2019 Blindness Detection Kaggle Dataset. This dataset exhibits an imbalanced image class distribution, which we addressed through appropriate balancing techniques. The performance of the considered models was assessed in terms of accuracy. The results showed that the hybrid network achieved an accuracy of 79.50%, while the DenseNet 121 model achiev...

Diabetic retinopathy detection through deep learning techniques: A review

Informatics in Medicine Unlocked , 2020

Diabetic Retinopathy (DR) is a common complication of diabetes mellitus, which causes lesions on the retina that effect vision. If it is not detected early, it can lead to blindness. Unfortunately, DR is not a reversible process, and treatment only sustains vision. DR early detection and treatment can significantly reduce the risk of vision loss. The manual diagnosis process of DR retina fundus images by ophthalmologists is time-, effort-, and costconsuming and prone to misdiagnosis unlike computer-aided diagnosis systems. Recently, deep learning has become one of the most common techniques that has achieved better performance in many areas, especially in medical image analysis and classification. Convolutional neural networks are more widely used as a deep learning method in medical image analysis and they are highly effective. For this article, the recent state-of-theart methods of DR color fundus images detection and classification using deep learning techniques have been reviewed and analyzed. Furthermore, the DR available datasets for the color fundus retina have been reviewed. Difference challenging issues that require more investigation are also discussed.

Deep Learning based Early Detection and Grading of Diabetic Retinopathy Using Retinal Fundus Images

ArXiv, 2018

Diabetic Retinopathy (DR) is a constantly deteriorating disease, being one of the leading causes of vision impairment and blindness. Subtle distinction among different grades and existence of many significant small features make the task of recognition very challenging. In addition, the present approach of retinopathy detection is a very laborious and time-intensive task, which heavily relies on the skill of a physician. Automated detection of diabetic retinopathy is essential to tackle these problems. Early-stage detection of diabetic retinopathy is also very important for diagnosis, which can prevent blindness with proper treatment. In this paper, we developed a novel deep convolutional neural network, which performs the early-stage detection by identifying all microaneurysms (MAs), the first signs of DR, along with correctly assigning labels to retinal fundus images which are graded into five categories. We have tested our network on the largest publicly available Kaggle diabetic...