An Efficient End-to-end Convolutional Neural Network for Classification of Diabetic Retinopathy using ResNet

Diabetic retinopathy (DR) is an eye disease which is caused by high blood sugar and high blood pressure and damage the blood vessels in the back (retina) of the eye. Diabetic retinopathy (DR) is not a reversible process and treatment only sustain vision. The number of ophthalmologists cannot meet the growing demands around the world. This study focuses on the automatic diagnosis of the disease through deep learning. Convolutional neural network (CNN) is more widely used as a deep learning method in medical image analysis. Multiclass image classification of images into non referable DR and referable DR has been done using proposed Convolutional neural network (CNN) model. For multiclass classification problem, the sensitivity, specificity and F2 score value for class 0 (no DR) are 81.75 %, 91.06 % and 81.80 % respectively. Whereas for class 1 (non-severe DR), sensitivity, specificity and F2 score values are 71.28 %, 78.52 % and 70.01 % respectively. Similarly for class 2 (sever DR), sensitivity, specificity and F2 score values are 73.03 %, 93.39 % and 75.08 % respectively. Bayesian optimization has been performed for tuning learning rate and gives optimal learning rate 0.000358 through the optimization process. The customized CNN is then trained using 0.000358 learning rate and then tested on test data The images in dataset have poor contrast and consists of impulse noises. Contrast limited adaptive histogram equalization (CLAHE) method is used to improve the contrast of the image followed by median filter to remove noise present in DR image.

This project proposes machine learning methods for detecting diabetic retinopathy by using CNN. The classification of retinal lesions from non-lesions using CNN classifiers is examined. Retinal retina images can be evaluated using machine learning-based medical image analysis. The image is pre-processed using the Contrast limited adaptive histogram equalization (CLAHE) filter. CLAHE is used to improve the visibility level of blurry images. The image is segmented using the FCM method to generate an appropriate threshold value for data segmentation. The method of feature extraction is implemented by RLC. The Gray Level Co – occurrence Matrix (GLCM) method is used for extracting statistical skin parameters in a given direction and distance. The use of convolutional neural network (CNN) algorithms has facilitated the early identification of diabetic retinopathy. This project provides excellent specificity and sensitivity for classifying images as with or without diabetic retinopathy. This project is implemented using MATLAB software.

Diabetic Retinopathy (DR) is eye disease caused through the retinal damage induced through long-term diabetes mellitus illness. In the initial stage, DR has minor vision difficulties or no symptoms but it outputs in vision loss if it is not treated early. However, overfitting occurs when a model learns noise or specific features from a small dataset which leading to poor generalization. To solve this issue, this research proposed an Otsu Multi-level Adaptive Thresholding with Deep Convolution Neural Network (OMAT+DCNN) approach for DR segmentation and classification. The Contrast Limited Adaptive Histogram Equalization (CLAHE) is used for preprocessing input image which enhances the contrast level and image visibility without amplifying noise. The OMAT is used for segmenting the affected regions and ResNet50 is used for extracting Deep Learning (DL) features. Then, relevant features are selected through Multi-Verse Optimizer (MVO) and DCNN is used for classification. The OMAT improves segmentation accuracy by considering multiple thresholds which adopts to local intensity variations within the image, even when the dataset is limited. DCNN capturing intricate patterns and spatial hierarchies within images and its deep architecture allows to learn complex representations which leads to enhance accuracy. The parameters like dice coefficient, Intersection over Union (IoU), Mean IoU (MIoU), precision, accuracy, f1-score and sensitivity are used for assessing performance. The OMAT+DCNN reaches 0.993 of accuracy when compared to existing techniques such as Convolutional Transformer Network (CTNet) and Residual Attention Network (RAN).

Diabetic-caused retinopathy is a disease that damages the blood vessels that are present in the part of retina of the eyes and might lead to permanent blindness in people who have diabetes. Ophthalmologists use the fundus images of the eyes, which are the patients identify this disease. However, manual detection of this abnormality is very time taking, cost-consuming, and has a higher probability of resulting in errors. Thus, to prevent this, a Deep Learning model is proposed to detect Diabetic caused retinopathy by using high-resolution fundus images which as a result decreases the chances of misdiagnosis. Initially, fundus images are pre-processed by using the Contrast limited adaptive histogram equalization (CLAHE) model and histogram-based segmentation model. In the next step, features from the pre-processed images are extracted. However, it becomes very difficult for the individual models to identify the behind features that are complicated and those can only classify Diabetic Retinopathy's various stages at low accuracy. Thus, to resolve this an ensemble of three deep Convolution Neural Network (CNN) models (Inceptionv3, Xception, and DenseNet121) is developed which will encode the rich features and improve the classification for different stages of DR like normal, mild, moderate, severe and PDR (Proliferative Diabetic Retinopathy). The results show that the proposed ensemble model detects all the Diabetic Retinopathy stages, not like the individual models and performs better comparatively. This improvement can thus reduce time, effort, and labor in healthcare services and will also increase the final decision accuracy.

The most common causes of blindness in adults worldwide, 2.6% of them are caused by diabetic retinopathy, which is a progressive disease caused by complications of diabetes mellitus. Early detection and prompt treatment help save eyesight. The artificial intelligence technology can provide objective and accurate screening results. Deep learning, especially the Convolutional Neural Network (CNN), which is part of artificial intelligence, has proven successful in solving image problems and is very well used in medical image analysis. CNN works well on large datasets, but it will affect network performance to overfitting in fewer datasets. So to solve the problem of small data, data augmentation techniques can be used. There are various kinds of data augmentation techniques. This study used the CNN method to classify diabetic retinopathy disease, compared several suitable data augmentation techniques for retinal fundus images, and used Contrast Limited Adaptive Histogram Equalization (CLAHE) for image enhancement. This study found that the augmented random zoom technique, together with CLAHE, provided the best accuracy of 98% with 96% sensitivity and 100% specificity.

In recent days, progressive rate of diabetic retinopathy (DR) becomes high and it is needed to develop an automated model for effective diagnosis of DR. This paper presents a new deep neural network with moth search optimization (DNN-MSO) algorithm based detection and classification model for DR images. The presented DNN-MSO algorithm involves different processes namely preprocessing, segmentation, feature extraction and classification. Initially, the contrast level of the DR images is improved using contrast limited adaptive histogram equalization model. After that, the preprocessed images are segmented using histogram approach. Then, Inception-ResNet V2 model is applied for feature extraction. Finally, extracted feature vectors are given to the DNN-MSO based classifier model to classify the different stages of DR. An extensive series of experiments were carried out and the results are validated on Messidor DR dataset. The obtained experimental outcome stated the superior characteristics of the DNN-MSO model by attaining a maximum accuracy, sensitivity and specificity of 99.12%, 97.91% and 99.47% respectively.

Diabetic retinopathy is a kind of disorder which occurs due to high blood sugar level. This disorder affects retina in many ways. Blood vessels in the retina gets altered . Exudates are secreted, hemorrhages occur, swellings appear in the retina. Diabetic Retinopathy (DR) is the major cause of blindness. Automatic Recognition of DR lesions like Exudates, in digital fundus images can contribute to the diagnosis and screening of this disease. In this approach, an automatic and efficient method to detect the exudates is proposed. The real time retinal images are obtained from a nearby hospital. The retinal images are pre-processed via. Contrast Limited Adaptive Histogram Equalization (CLAHE). The preprocessed colour retinal images are segmented using K-Means Clustering technique. The segmented images establish a dataset of regions. To classify these segmented regions into Exudates and Non-Exudates, a set of features based on colour and texture are extracted. Classification is done using support Vector Machine This method appears promising as it can detect the very small areas of exudates. Keywords - Diabetic Retinopathy, Exudates, fundus image, k means clustering, SVM I. INTRODUCTION The retina is the inner and most important layer of the eye. It is composed of several important anatomical structures which can indicate various diseases. Cardiovascular disease such as stroke and myocardial infarcation can be identified from retinal blood vessels. Diabetic Retinopathy is the common retinal complication associated with diabetes. It is a major cause of blindness in both middle and advanced age groups. The International Diabetes Federation reports that over 50 million people in India have this disease and it is growing rapidly (IDF 2009a) (2). The estimated prevalence of diabetes for all age groups worldwide was 2.8% in 2000 and 4.4% in 2030 meaning that the total number of diabetes patients is forecasted to rise from 171 million in 2000 to 366 million in 2030 (3). Therefore regular screening is the most efficient way of reducing the vision loss. Diabetic Retinopathy is mainly caused by the changes in the blood vessels of the retina due to

Diabetic Retinopathy (DR) has become a leading cause of blindness among diabetic patients. Accurate and timely diagnosis of DR is critical to slowing disease progression. This research proposes a Hybrid Convolutional Neural Network (CNN)-based model, named Diabetic Retinopathy Detection Network (DRD-Net). The proposed DRD-Net designed to enhance diagnostic accuracy by addressing key challenges such as gradient vanishing and lesion scale variability in fundus images. Contrast-Limited Adaptive Histogram Equalization (CLAHE) was used to enhance contrast and highlight lesions in fundus images. To increase the diversity of training samples, the proposed framework employs geometric data augmentation techniques. DRD-Net incorporates the Swish activation function along with densely connected blocks to mitigate gradient vanishing and enhancing feature propagation within the network. Additionally, the model integrates two Inception blocks to facilitate multiscale feature extraction, which is essential for detecting small Regions of Interest (RoI) in fundus images. Experimental results demonstrate that DRD-Net achieves a precision of 84.4%, recall of 84.5%, F1-score of 84.1%, and accuracy of 85.1%, outperforming several state-of-the-art models on the IDRiD dataset. These results highlight DRD-Net’s potential as an effective solution for automated DR diagnosis, contributing to more efficient and accurate DR screening.

Diabetes is one of the most common illnesses, and diabetes causes Diabetic Retinopathy (DR), which can lead to vision problems and blindness if not detected early. Early detection of DR is possible with the help of a computer-aided diagnostic (CAD) system, thus preventing many people from losing their eyesight. In order to classify fundus images into five categories, this article makes use of soft attention as feature refinement in convolutional neural networks (CNN). The proposed framework initially utilizes Contrast Limited Adaptive Histogram Equalization (CLAHE) as a pre-processing technique for improving the contrast level of the fundus images and then applied concatenation based CNN architecture coupled with soft attention for feature extraction and classification. The data has been balanced using oversampling so that each DR grade contains equal number of samples during the training process. The experimental results reveal that, unlike current methods, the proposed model detects all the five stages of DR.

Several diseases have adverb effect on visual system of human visual system (eye) and diabetes is leading one among them. Prolong and uncontrolled diabetic patient is prone to Diabetic Retinopathy (DR). DR is asymptomatic disease hence requires detection in early stages to avoid big loss in vision. It shows immediate necessity of screening system to access eye images and analyze for DR detection. Microaneurysms (MAs) are primary lesion of DR, so their detection can give time for patient and ophthalmologist to prevent further vision loss. Region growing segmentation method is proposed for accurate detection of MAs. The preprocessing of retinal images uses non local means (NLM) filter and contrast limited adaptive histogram equalization (CLAHE) for noise removal and enhancement image quality. In segmentation, region growing algorithm in which the seeds for the grower are selected and positioned by means of Forstner Corner Detection theory is utilized. After segmentation, the redundant areas are removed using morphological operations (Niblack Adaptive Thresholding) and finally the Predator prey optimizer is used for optimizing the features for MA detection.

Diabetic retinopathy (DR) is a leading cause of blindness worldwide, affecting millions of dia- betic patients. Early detection and accurate classification of DR stages are crucial for preventing vision loss and improving patient outcomes. This survey paper presents a comprehensive anal- ysis of deep learning approaches for automatic detection and classification of diabetic retinopa- thy from fundus images. The proposed system utilizes Faster Region-based Convolutional Neural Networks (Faster R-CNN) architecture to accurately identify and categorize the sever- ity of DR in retinal images. The methodology encompasses image acquisition using fundus cameras, preprocessing for quality enhancement, deep feature learning for data-driven char- acterizations, and classification into various DR stages including No DR, Mild DR, Moderate DR, Proliferative DR, and Severe DR. This automated approach offers significant advantages over traditional manual screening methods, providing faster, more consistent, and cost-effective diagnosis capabilities. The technical implementation focuses on developing robust algorithms for fundus image analysis, including preprocessing techniques for image enhancement, feature extraction using convolutional neural networks, and classification models optimized for medical imaging appli- cations. The system addresses challenges such as image quality variations, lighting conditions, and anatomical differences across patients. Performance evaluation encompasses accuracy met- rics, sensitivity, specificity, and area under the ROC curve (AUC) to ensure clinical reliability. The proposed Faster R-CNN architecture demonstrates superior performance compared to tra- ditional methods, achieving high accuracy in DR detection while maintaining computational efficiency suitable for clinical deployment. This automated screening system has the potential to significantly improve early detection rates, reduce healthcare costs, and enhance patient care quality in diabetic retinopathy management.

Diabetic retinopathy (DR) is one of the leading causes of vision loss in adults and is one of the detrimental side effects of the mass prevalence of Diabetes Mellitus (DM). It is crucial to have an efficient screening method for early diagnosis of DR to prevent vision loss. This paper compares and analyzes the various Machine Learning (ML) techniques, from traditional ML to advanced Deep Learning models. We compared and analyzed the efficacy of Convolutional Neural Networks (CNNs), Capsule Networks (CapsNet), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), decision trees, and Random Forests. This paper also considers determining factors in the evaluation, including contrast enhancements, noise reduction, grayscaling, etc We analyze recent research studies and compare methodologies and metrics, including accuracy, precision, sensitivity, and specificity. The findings highlight the advanced performance of Deep Learning (DL) models, with CapsNet achieving a remarkable accuracy of up to 97.98% and a high precision rate, outperforming other traditional ML methods. The Contrast Limited Adaptive Histogram Equalization (CLAHE) preprocessing technique substantially enhanced the model’s efficiency. Each ML method’s computational requirements are also considered. While most advanced deep learning methods performed better according to the metrics, they are more computationally complex, requiring more resources and data input. We also discussed how datasets like MESSIDOR could be more straightforward and contribute to highly evaluated performance and that there is a lack of consistency regarding benchmark datasets across papers in the field. Using the DL models facilitates accurate early detection for DR screening, can potentially reduce vision loss risks, and improves accessibility and cost-efficiency of eye screening. Further research is recommended to extend our findings by building models with public datasets, experimenting with ensembles of DL and traditional ML models, and considering testing high-performing models like CapsNet.

In the recent era, the increasing diabetic ratio effects in producing complex retinal diseases. The early diagnosis will avoid the consequences of diabetic-related complications. So, there is an urge to develop a computerized model for identifying the retinopathy features caused due to diabetes while evading the false positives. The aim of this paper is to identify and segment the retinal features like blood vessels, optic disc in diabetic retinopathy (DR) fundus images in order to remove the non-diabetic retinopathy features which makes the detection of DR features (lesions) easier. And this is done by using a hybrid segmentation algorithm called BINI which combines the model of both binary and Niblack’s thresholding. Initially, the input image is standardized by resizing the image using bi-cubic interpolation area method. Then, the fundus image quality is enhanced using preprocessing techniques like green channel extraction, intensity channel extraction, median filtering, contrast limited adaptive histogram equalization and morphological operations. The preprocessed images are segmented to produce retinal features using the BINI algorithm. It is a novel method which hybrids both binary thresholding and Niblack’s thresholding. The performance of the segmentation methods is evaluated using the validation measures like Rand index (Ri) and Jaccard index (Ji), Precision (Pr), Recall (Rc) and F-Measure (Fm). The proposed method for segmenting the retinal features using BINI thresholding has given an accuracy of about 96.48%; it leads to an accuracy of 100% clear segmentation of the lesions of diabetic retinopathy images.

Diabetic retinopathy (DR), a frequent complication of diabetes mellitus, is the prime reason for vision loss worldwide. The current method for identifying diabetic retinopathy is a time-consuming and manual process, requiring trained medical professionals to examine digital color fundus photographs of the retina and look for the presence of lesions associated with the disease’s vascular abnormalities. Despite technological advancements, accurately identifying the early stages of diabetic retinopathy remains a challenge. To address this issue, different computer vision-based approaches have been introduced to detect DR and classify various stages from retina images. However, these techniques have limited success, particularly in accurately identifying the early stages of the disease. In this study, we employed a publicly available Kaggle Eyepac dataset to train our model for the detection of DR which is known to have five distinct stages, namely, proliferate DR, severe DR, moderate DR, mild DR, and no DR. The overall accuracy, precision, recall, Fl-score, and AUC score obtained by our proposed model were 88.31± 0.23,89.15± 0.19,90.25± 0.41,89.69± 0.25, and 87.38± 1.45 respectively. The performance of our model is improved by employing a pre-processing technique known as Contrast Limited Adaptive Histogram Equalization (CLAHE) followed by a state-of-the-art deep learning model known as ConvNeXt to achieve promising results.

Designing and developing automated systems to detect and grade Diabetic Retinopathy (DR) is one of the recent research areas in the world of medical image applications since it is considered one of the main causes of total blindness for people who have diabetes in the mid-age. In this paper, a complete pipeline for retinal fundus images processing and analysis has been described, implemented and evaluated. This pipeline has three main stages: (i) image pre-processing, (ii) features extraction and (iii) classification. In the first stage, the image has been pre-processed using different transformations to standardize the images and to enhance the images quality. It has been proven that Gaussian filtering is quite effective in this context to enhance the images contrast. In the second and third stage, the convolution neural network (CNN), one of the best neural network architecture for image analysis applications, has been used. The concept of transfer learning and fine tuning have been advocated in this paper and applied for ResNet18 using the publicly available Kaggle dataset. The problem of DR diagnosis has been handled as a multi-class classification problem where there are five levels of the disease severity (0 – No DR, 1 – Mild, 2 – Moderate, 3 – Severe, 4 – Proliferative DR). The final model has achieved accuracy of 70 %, recall of 50% and specificity of 88% outperforming other models built from scratch with less training time and proving the efficiency of transfer learning in this context. The training process has considered the problem of imbalanced dataset using two different ways and it has been discovered that using imbalanced dataset sampler is a very efficient solution. The final model developed in this research could be used as the main unit for a computer aided system to be hosted online for DR detection and diagnosis.