Classification Of Eye Diseases Research Articles

EYE-YOLO: a multi-spatial pyramid pooling and Focal-EIOU loss inspired tiny YOLOv7 for fundus eye disease detection

PurposeThe purpose of this work is to present an approach for autonomous detection of eye disease in fundus images. Furthermore, this work presents an improved variant of the Tiny YOLOv7 model developed specifically for eye disease detection. The model proposed in this work is a highly useful tool for the development of applications for autonomous detection of eye diseases in fundus images that can help and assist ophthalmologists.Design/methodology/approachThe approach adopted to carry out this work is twofold. Firstly, a richly annotated dataset consisting of eye disease classes, namely, cataract, glaucoma, retinal disease and normal eye, was created. Secondly, an improved variant of the Tiny YOLOv7 model was developed and proposed as EYE-YOLO. The proposed EYE-YOLO model has been developed by integrating multi-spatial pyramid pooling in the feature extraction network and Focal-EIOU loss in the detection network of the Tiny YOLOv7 model. Moreover, at run time, the mosaic augmentation strategy has been utilized with the proposed model to achieve benchmark results. Further, evaluations have been carried out for performance metrics, namely, precision, recall, F1 Score, average precision (AP) and mean average precision (mAP).FindingsThe proposed EYE-YOLO achieved 28% higher precision, 18% higher recall, 24% higher F1 Score and 30.81% higher mAP than the Tiny YOLOv7 model. Moreover, in terms of AP for each class of the employed dataset, it achieved 9.74% higher AP for cataract, 27.73% higher AP for glaucoma, 72.50% higher AP for retina disease and 13.26% higher AP for normal eye. In comparison to the state-of-the-art Tiny YOLOv5, Tiny YOLOv6 and Tiny YOLOv8 models, the proposed EYE-YOLO achieved 6–23.32% higher mAP.Originality/valueThis work addresses the problem of eye disease recognition as a bounding box regression and detection problem. Whereas, the work in the related research is largely based on eye disease classification. The other highlight of this work is to propose a richly annotated dataset for different eye diseases useful for training deep learning-based object detectors. The major highlight of this work lies in the proposal of an improved variant of the Tiny YOLOv7 model focusing on eye disease detection. The proposed modifications in the Tiny YOLOv7 aided the proposed model in achieving better results as compared to the state-of-the-art Tiny YOLOv8 and YOLOv8 Nano.

A novel fusion of genetic grey wolf optimization and kernel extreme learning machines for precise diabetic eye disease classification.

In response to the growing number of diabetes cases worldwide, Our study addresses the escalating issue of diabetic eye disease (DED), a significant contributor to vision loss globally, through a pioneering approach. We propose a novel integration of a Genetic Grey Wolf Optimization (G-GWO) algorithm with a Fully Convolutional Encoder-Decoder Network (FCEDN), further enhanced by a Kernel Extreme Learning Machine (KELM) for refined image segmentation and disease classification. This innovative combination leverages the genetic algorithm and grey wolf optimization to boost the FCEDN's efficiency, enabling precise detection of DED stages and differentiation among disease types. Tested across diverse datasets, including IDRiD, DR-HAGIS, and ODIR, our model showcased superior performance, achieving classification accuracies between 98.5% to 98.8%, surpassing existing methods. This advancement sets a new standard in DED detection and offers significant potential for automating fundus image analysis, reducing reliance on manual examination, and improving patient care efficiency. Our findings are crucial to enhancing diagnostic accuracy and patient outcomes in DED management.

Eye Disease Classification Based On Fundus Image Using Yolo V8 Algorithm

Eye disease is a very serious problem because it affects one of the five human senses. In many cases many people ignore the impact of eye diseases in the early stages. In general, the process of examining eye diseases is carried out based on manual analysis by doctors (experts) on the fundus image of the patient's eye at a fairly expensive cost. To overcome this, the author proposes an eye disease classification system that can automatically detect eye diseases using YOLO V8. This system can be used for early detection of eye diseases to prevent the development of more serious eye diseases. From the test results of the model built, the accuracy value is 92%, precision is 91%, recall is 92%, F1-score is 91%. Overall, these results can be considered satisfactory and can be implemented for eye disease classification systems based on fundus images.

The classification of eye diseases from fundus images based on CNN and pretrained models

Visual impairment affects more than a billion people worldwide due to insufficient care or inadequate vision screening. Computer-aided diagnosis using deep neural networks is a promising approach, it can analyse and process retinal fundus images, providing valuable reference data for doctors in clinical diagnosis or screening. This study aims to achieve an accurate classification of fundus images, including images of healthy patients as well as those with diabetic retinopathy, cataracts, and glaucoma, using a convolutional neural network (CNN) architecture and several pretrained models (AlexNet, GoogleNet, ResNet18, ResNet50, YOLOv3, and VGG 19). To enhance the training process, a mirror effect technique was applied to augment the volume of data. The experimental study resulted in very satisfactory outcomes, with the GoogleNet model paired with the SGDM optimiser achieving the highest accuracy (92.7 %).

Deep learning-based classification of eye diseases using Convolutional Neural Network for OCT images

Deep learning shows promising results in extracting useful information from medical images. The proposed work applies a Convolutional Neural Network (CNN) on retinal images to extract features that allow early detection of ophthalmic diseases. Early disease diagnosis is critical to retinal treatment. Any damage that occurs to retinal tissues that cannot be recovered can result in permanent degradation or even complete loss of sight. The proposed deep-learning algorithm detects three different diseases from features extracted from Optical Coherence Tomography (OCT) images. The deep-learning algorithm uses CNN to classify OCT images into four categories. The four categories are Normal retina, Diabetic Macular Edema (DME), Choroidal Neovascular Membranes (CNM), and Age-related Macular Degeneration (AMD). The proposed work uses publicly available OCT retinal images as a dataset. The experimental results show significant enhancement in classification accuracy while detecting the features of the three listed diseases.

Review of Eye Diseases Detection and Classification Using Deep Learning Techniques

Automated diagnosis of eye diseases using machine and deep learning models has become increasingly popular. Glaucoma, cataracts, diabetic retinopathy, Myopia, and age-related macular degeneration are common eye diseases that can cause severe damage. It is crucial to detect eye diseases early to prevent any potentially serious consequences. Early detection of eye disease is vital for effective treatment. Doing in-depth reading to identify any potential signs of eye disease is highly recommended. This paper will review all machine learning models built to detect and classify eye diseases in addition to helping grasp all limitations and challenges in this field. Recognizing eye diseases is a difficult task that typically requires several years of medical experience. This research is to be conducted to serve as a starting point for finding the most versatile solution. This research aims to review eye disease classification using deep learning models, including VGG16, ResNet, and Inception. The general classification model consists of these steps: The first step is to collect the globally obtainable datasets for the eye disease and pre-process them to ensure the generalization of experiments. The goal is to train the model to recognize disease symptoms instead of tweaking the outcomes for a specific dataset section. With the successful deployment of deep learning techniques for image classification and object recognition, research is now directed towards deep learning techniques instead of traditional handcrafted methods. One possible solution for the eye diseases classification challenge is to use a pre-trained deep CNN model for representation and feature extraction. This solution can be followed by classifier methods, such as support vector machines (SVM), multilayer perceptron (MLP), etc. It has been detected that CNN-based methods learned on large-scale marked datasets can be used for eye disease classification tasks with limited training datasets.

Eye Disease Net: an algorithmic model for rapid diagnosis of diseases.

With the development of science and technology and the improvement of the quality of life, ophthalmic diseases have become one of the major disorders that affect the quality of life of people. In view of this, we propose a new method of ophthalmic disease classification, ED-Net (Eye Disease Classification Net), which is composed of the ED_Resnet model and ED_Xception model, and we compare our ED-Net method with classical classification algorithms, transformer algorithm, more advanced image classification algorithms and ophthalmic disease classification algorithms. We propose the ED_Resnet module and ED_Xception module and reconstruct these two modules into a new image classification algorithm ED-Net, and compared them with classical classification algorithms, transformer algorithms, more advanced image classification algorithms and eye disease classification algorithms.

Deep learning innovations in diagnosing diabetic retinopathy: The potential of transfer learning and the DiaCNN model

Diabetic retinopathy (DR) is a significant cause of vision impairment, emphasizing the critical need for early detection and timely intervention to avert visual deterioration. Diagnosing DR is inherently complex, as it necessitates the meticulous examination of intricate retinal images by experienced specialists. This makes the early diagnosis of DR essential for effective treatment and prevention of eventual blindness. Traditional diagnostic methods, relying on human interpretation of medical images, face challenges in terms of accuracy and efficiency. In the present research, we introduce a novel method that offers superior precision in DR diagnosis, compared to traditional methods, by employing advanced deep learning techniques. Central to this approach is the concept of transfer learning. This entails the utilization of pre-existing, well-established models, specifically InceptionResNetv2 and Inceptionv3, to extract features and fine-tune selected layers to cater to the unique requirements of this specific diagnostic task. Concurrently, we also present a newly devised model, DiaCNN, which is tailored for the classification of eye diseases. To prove the efficacy of the proposed methodology, we leveraged the Ocular Disease Intelligent Recognition (ODIR) dataset, which comprises eight different eye disease categories. The results are promising. The InceptionResNetv2 model, incorporating transfer learning, registered an impressive 97.5% accuracy in both the training and testing phases. Its counterpart, the Inceptionv3 model, achieved an even more commendable 99.7% accuracy during training, and 97.5% during testing. Remarkably, the DiaCNN model showcased unparalleled precision, achieving 100% accuracy in training and 98.3% in testing. These figures represent a significant leap in classification accuracy when juxtaposed with existing state-of-the-art diagnostic methods. Such advancements hold immense promise for the future, emphasizing the potential of our proposed technique to revolutionize the accuracy of DR and other eye disease diagnoses. By facilitating earlier detection and more timely interventions, this approach stands poised to significantly reduce the incidence of blindness associated with DR, thus heralding a new era of improved patient outcomes. Therefore, this work, through its novel approach and stellar results, not only pushes the boundaries of DR diagnostic accuracy but also promises a transformative impact in early detection and intervention, aiming to substantially diminish DR-induced blindness and champion enhanced patient care.

Performance Metrics of AdaBoost and Random Forest in Multi-Class Eye Disease Identification: An Imbalanced Dataset Approach

This study presents a comprehensive evaluation of AdaBoost and Random Forest Classifier algorithms in the classification of eye diseases, focusing on a challenging scenario involving an imbalanced dataset. Eye diseases, particularly Cataract, Diabetic Retinopathy, Glaucoma, and Normal eye conditions, pose significant diagnostic challenges, and the advent of machine learning offers promising avenues for enhancing diagnostic accuracy. Our research utilizes a dataset preprocessed with Canny edge detection for image segmentation and Hu Moments for feature extraction, providing a robust foundation for the comparative analysis. The performance of the algorithms is assessed using a 5-fold cross-validation approach, with accuracy, precision, recall, and F1-score as the key metrics. The results indicate that the Random Forest Classifier outperforms AdaBoost across these metrics, albeit with moderate overall performance. This finding underscores the potential and limitations of using advanced machine learning techniques for medical image analysis, particularly in the context of imbalanced datasets. The study contributes to the field by providing insights into the effectiveness of different machine learning algorithms in handling the complexities of medical image classification. For future research, it recommends exploring a diverse range of image processing techniques, delving into other sophisticated machine learning models, and extending the study to encompass a wider array of eye diseases. These findings have practical implications in guiding the selection of machine learning tools for medical diagnostics and highlight the need for continuous improvement in automated systems for enhanced patient care.

Artificial Intelligence-Driven Eye Disease Classification Model

Eye diseases can result in various challenges and visual impairments. These diseases can affect an individual’s quality of life and general health and well-being. The symptoms of eye diseases vary widely depending on the nature and severity of the disease. Early diagnosis can protect individuals from visual impairment. Artificial intelligence (AI)-based eye disease classification (EDC) assists physicians in providing effective patient services. However, the complexities of the fundus image affect the classifier’s performance. There is a demand for a practical EDC for identifying eye diseases in the earlier stages. Thus, the author intends to build an EDC model using the deep learning (DL) technique. Denoising autoencoders are used to remove the noises and artifacts from the fundus images. The single-shot detection (SSD) approach generates the key features. The whale optimization algorithm (WOA) with Levy Flight and Wavelet search strategy is followed for selecting the features. In addition, the Adam optimizer (AO) is applied to fine-tune the ShuffleNet V2 model to classify the fundus images. Two benchmark datasets, ocular disease intelligent recognition (ODIR) and EDC datasets, are utilized for performance evaluation. The proposed EDC model achieved accuracy and Kappa values of 99.1 and 96.4, and 99.4 and 96.5, in the ODIR and EDC datasets, respectively. It outperformed the recent EDC models. The findings highlight the significance of the proposed EDC model in classifying eye diseases using complex fundus images. Healthcare centers can implement the proposed model to improve their standards and serve a more significant number of patients. In the future, the proposed model can be extended to identify a comprehensive range of eye diseases.

Analysis of Eye Disease Classification by Comparison of the Random Forest Method and K-Nearest Neighbor Method

Eye disease is a serious issue all over the world, and image-based classification systems play an important role in the early detection and management of eye disease. This research compares the performance between Random Forest (RF) and K-Nearest Neighbor (KNN) classification models in identifying eye disorders using image datasets divided into four classes: "normal," "glaucoma," "cataract," and "diabetic retinopathy."Â Â The dataset is converted into a feature vector and then divided into training data and test data subsets. The analysis results show that the RF model achieved an accuracy level of 80%, whereas the KNN model achieved 70%. Based on these findings, it is possible to conclude that the RF model outperforms the other models in categorizing the types of eye illnesses in the dataset. A Python-based website was also built utilizing the Flask framework to build an interactive and real-time eye illness diagnosis system. Users can upload photos of their retinas to this website and quickly receive eye disease detection results. The adoption of this technology has a tremendous impact, making eye disease detection solutions more accessible. Furthermore, this solution plays an important role in the early detection and effective management of eye health cases.

Classification of eye diseases in fundus images using Convolutional Neural Network (CNN) method with EfficientNet architecture

This research is designed to classify eye disease conditions into three classes, namely normal, cataract, and glaucoma using a system. The system will use Convolutional Neural Network (CNN) with EfficientNet architecture. The EfficientNet that will be used is EfficientNet-B0. The dataset in this paper is obtained from Kaggle totaling 300 images. From this data, augmentation is carried out so that 3.600 images are obtained consisting of "normal" (1200 images), "cataract" (1200 images), and "glaucoma" (1200 images). This data is processed into 4 different datasets, namely the original dataset, augmentation dataset, augmentation dataset that has been preprocessed grayscale, and augmentation dataset that has been preprocessed thresholding. The best results are obtained using the Adam optimizer, learning rate 0.00001, and batch size 32, and iterations of 20 epochs. The best dataset is the augmentation dataset that has been preprocessed grayscale with an accuracy of 79.22%, precision value of 80.3%, recall value of 79.22%, F1-Score of 78.87%.

Classification of Myopia Levels using Deep Learning Methods on Fundus Image

Disorders of the eye or also known as eye disease is a condition that can affect vision for some people in their lifetime. There are 40 types of eye disorders or eye diseases, one of which is Myopia. Myopia is a visual disturbance that causes objects that are far away to appear blurry, but there is no problem seeing objects that are near. Myopia or nearsightedness is also known as minus eye. From this description, it is very important to conduct research in detecting eye diseases before the increase in eye minus and blindness. This study aims to classify myopic eye disease using the Deep Learning method with several different architectures, namely the VGG16, VGG19 and InceptionV3V3 models. Where the first is to distinguish normal and abnormal while the other is to classify with Augmented myopia image dataset and non augmented myopia image dataset obtained from the Retinal Fundus Multi-Disease Image Dataset (RFMID). In the implementation of the Deep Learning method using 20 Epochs. The results of the accuracy of the classification of eye diseases using the non augmented myopia image dataset are 66.0% for the VGG16 architectural model, then 95.99% for the VGG19 architectural model and 93.99% for the InceptionV3 architectural model and the accuracy results using the Augmented myopia image dataset are 97.53% for the VGG16 architectural model, 97.53% for the VGG19 architectural model and 99.50% for the InceptionV3 architecture model.

Clinical Decision Support System for Ophthalmologists for Eye Disease Classification

ABSTRACT In medical applications, ocular OCT (optical coherence tomography) is used to assess glaucoma, macular degeneration, diabetic macular edema and other eye diseases as it is capable of showing the cross-sections of tissue layers. The creation of new blood vessels in the choroid layer of the eye is known as choroidal neovascularization (CNV). The aging and macular degeneration will represent the symptom DRUSEN. Our sharp central vision is affected due to DRUSEN. An irreversible vision loss is caused in diabetic patients due to diabetic macular edema (DME). It is mainly due to the leaking of blood vessels in the retina. This research work focus on designing a clinical decision support system to assist the ophthalmologist in classifying the three different types of eye diseases. The existing nine pre-trained CNN models are used for this purpose. The extracted features are used to generate the trained model that is further used for eye disease classification. The training accuracy, validation accuracy, training loss and validation loss are computed for 100 iterations for each pre-trained CNN models during training and validation. The trained model obtained after training is used as input to the classifier, which classifies the images under-diagnosis into NORMAL (normal eye), CNV, DME and DRUSEN. The performance metrics of the classifier designed using each pre-trained models are evaluated and compared for four classes independently. The test results show that the performance of the classifier implemented using the pre-trained model InceptionV3 is better than all other models.

Optimized convolutional neural network-based comprehensive early diagnosis method for multiple eye disease recognition

The multilabel classification method (MCM) is an active and effective research area in image analysis for the detection of various diseases. MCM provides insights and assistance to ophthalmologists in the detection of eye disease at an early stage. We propose comprehensive age-related eye disease detection at an early stage using retinal fundus images that were taken from the online public dataset. The flower pollination optimization algorithm was used for the optimization of the hyperparameters of the deep convolutional neural network (DCNN), which increased the speed and accuracy of the network. Initially, training was performed using the public datasets. The overall improvement in training accuracy (7.5%) was achieved using the optimized method compared with the nonoptimized method. Then, the output of the DCNN was applied to a multiclass support vector machine for the classification of eye diseases. The performance of the proposed method was compared with that of the other optimization techniques with the help of the standard performance measures, namely accuracy, specificity, sensitivity, precision, and F1-score. Upon comparing the performance of the proposed method with that of the nonoptimized DCNN in terms of performance measures, it had an improvement in the detection accuracy of 9.62% for AMD, 3.57% for glaucoma, 6.59% for normal, 7.71% for DR, and 11.75% for cataract. The obtained results show the effectiveness of the proposed method.

Fast and Efficient Method for Optical Coherence Tomography Images Classification Using Deep Learning Approach.

The use of optical coherence tomography (OCT) in medical diagnostics is now common. The growing amount of data leads us to propose an automated support system for medical staff. The key part of the system is a classification algorithm developed with modern machine learning techniques. The main contribution is to present a new approach for the classification of eye diseases using the convolutional neural network model. The research concerns the classification of patients on the basis of OCT B-scans into one of four categories: Diabetic Macular Edema (DME), Choroidal Neovascularization (CNV), Drusen, and Normal. Those categories are available in a publicly available dataset of above 84,000 images utilized for the research. After several tested architectures, our 5-layer neural network gives us a promising result. We compared them to the other available solutions which proves the high quality of our algorithm. Equally important for the application of the algorithm is the computational time, which is reduced by the limited size of the model. In addition, the article presents a detailed method of image data augmentation and its impact on the classification results. The results of the experiments were also presented for several derived models of convolutional network architectures that were tested during the research. Improving processes in medical treatment is important. The algorithm cannot replace a doctor but, for example, can be a valuable tool for speeding up the process of diagnosis during screening tests.

Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model.

Diabetic retinopathy (DR) is an eye disease that alters the blood vessels of a person suffering from diabetes. Diabetic macular edema (DME) occurs when DR affects the macula, which causes fluid accumulation in the macula. Efficient screening systems require experts to manually analyze images to recognize diseases. However, due to the challenging nature of the screening method and lack of trained human resources, devising effective screening-oriented treatment is an expensive task. Automated systems are trying to cope with these challenges; however, these methods do not generalize well to multiple diseases and real-world scenarios. To solve the aforementioned issues, we propose a new method comprising two main steps. The first involves dataset preparation and feature extraction and the other relates to improving a custom deep learning based CenterNet model trained for eye disease classification. Initially, we generate annotations for suspected samples to locate the precise region of interest, while the other part of the proposed solution trains the Center Net model over annotated images. Specifically, we use DenseNet-100 as a feature extraction method on which the one-stage detector, CenterNet, is employed to localize and classify the disease lesions. We evaluated our method over challenging datasets, namely, APTOS-2019 and IDRiD, and attained average accuracy of 97.93% and 98.10%, respectively. We also performed cross-dataset validation with benchmark EYEPACS and Diaretdb1 datasets. Both qualitative and quantitative results demonstrate that our proposed approach outperforms state-of-the-art methods due to more effective localization power of CenterNet, as it can easily recognize small lesions and deal with over-fitted training data. Our proposed framework is proficient in correctly locating and classifying disease lesions. In comparison to existing DR and DME classification approaches, our method can extract representative key points from low-intensity and noisy images and accurately classify them. Hence our approach can play an important role in automated detection and recognition of DR and DME lesions.

Geometric and Volumetric Measurements of Orbital Structures in CT Scans in Thyroid Eye Disease Classification

This study examines the usefulness of both geometric and volumetric measurements of orbital soft tissues on CT scans to provide quantitative diagnostic guidance in image reading of thyroid eye disease (TED). Computed tomography (CT) images were obtained from 92 orbits and were classified as impaired motility (TED-IM) and normal motility (TED-NM). The TED-IM group was further divided into dysthyroid optic neuropathy (DON) and non-DON groups. There were 5 volumetric, 2 angular, and 3 ratio parameter measurements acquired from CT images to examine their feasibility in TED classification. We found that the mean volumes of extraocular muscle and retroorbital fat and their ratio to the orbital volume were significantly different between the two motility groups. The mean ratio of extraocular muscle volume in orbital apex and orbital apex volume (EMV-OA/OAV) was significantly larger in DON than non-DON patients (p < 0.05). The population distribution among TED-NM, non-DON, and DON groups significantly varied for different angles between the optic nerve and medial rectus and lateral rectus. In conclusion, geometric and volumetric measurements using CT scans help to quantitatively classify TED.

Towards Automated Eye Diagnosis: An Improved Retinal Vessel Segmentation Framework Using Ensemble Block Matching 3D Filter.

Automated detection of vision threatening eye disease based on high resolution retinal fundus images requires accurate segmentation of the blood vessels. In this regard, detection and segmentation of finer vessels, which are obscured by a considerable degree of noise and poor illumination, is particularly challenging. These noises include (systematic) additive noise and multiplicative (speckle) noise, which arise due to various practical limitations of the fundus imaging systems. To address this inherent issue, we present an efficient unsupervised vessel segmentation strategy as a step towards accurate classification of eye diseases from the noisy fundus images. To that end, an ensemble block matching 3D (BM3D) speckle filter is proposed for removal of unwanted noise leading to improved detection. The BM3D-speckle filter, despite its ability to recover finer details (i.e., vessels in fundus images), yields a pattern of checkerboard artifacts in the aftermath of multiplicative (speckle) noise removal. These artifacts are generally ignored in the case of satellite images; however, in the case of fundus images, these artifacts have a degenerating effect on the segmentation or detection of fine vessels. To counter that, an ensemble of BM3D-speckle filter is proposed to suppress these artifacts while further sharpening the recovered vessels. This is subsequently used to devise an improved unsupervised segmentation strategy that can detect fine vessels even in the presence of dominant noise and yields an overall much improved accuracy. Testing was carried out on three publicly available databases namely Structured Analysis of the Retina (STARE), Digital Retinal Images for Vessel Extraction (DRIVE) and CHASE_DB1. We have achieved a sensitivity of 82.88, 81.41 and 82.03 on DRIVE, SATARE, and CHASE_DB1, respectively. The accuracy is also boosted to 95.41, 95.70 and 95.61 on DRIVE, SATARE, and CHASE_DB1, respectively. The performance of the proposed methods on images with pathologies was observed to be more convincing than the performance of similar state-of-the-art methods.

Eye Diseases Classification Using Back Propagation With Parabola Learning Rate

Human eye disease classification adapted by many pieces of research in the last decade due to the importance of eye organ for humans and the evolution in classification techniques. Seven external eye diseases plus normal eye classified in this paper using backpropagation with linear cyclic learning rate-based parabola function. The classification accuracy achieved in this paper is (89.83%).