Classification Of Eye Diseases Research Articles

Advanced deep learning approaches for early detection and localization of ocular diseases

Rece with t advancements in modern technology have significantly enhanced the transmission of information, particularly in image processing, utilizing deep learning algorithms. This study aims to propose a a robust deep-learning strategy for detecting and recognizing eye defects and diseases from medical images. We present a detailed practical simulation of hybrid deep learning techniques designed for medical image classification based on multi-descriptor algorithms. The focus is on the classification of eye diseases by applying an advanced deep-learning algorithm to a dataset comprising various pathological eye conditions. Training operations for the proposed algorithm were conducted following the initialization phase, which included the extraction of multi-specification features. This enables the deep learning model to effectively analyze input eye images and accurately diagnose conditions. Our results demonstrate a diagnostic efficiency of 99%, with an error rate not exceeding 0.015%. The findings underscore the high efficiency and accuracy of deep learning algorithms in classifying and analyzing image data, thereby significantly reducing the workload for healthcare professionals.

Transfer Learning for Automated Classification of Eye Disease in Fundus Images from Pretrained Model

Transfer Learning for Automated Classification of Eye Disease in Fundus Images from Pretrained Model

Detection and diagnosis of diabetic eye diseases using two phase transfer learning approach.

Early diagnosis and treatment of diabetic eye disease (DED) improve prognosis and lessen the possibility of permanent vision loss. Screening of retinal fundus images is a significant process widely employed for diagnosing patients with DED or other eye problems. However, considerable time and effort are required to detect these images manually. Deep learning approaches in machine learning have attained superior performance for the binary classification of healthy and pathological retinal fundus images. In contrast, multi-class retinal eye disease classification is still a difficult task. Therefore, a two-phase transfer learning approach is developed in this research for automated classification and segmentation of multi-class DED pathologies. In the first step, a Modified ResNet-50 model pre-trained on the ImageNet dataset was transferred and learned to classify normal diabetic macular edema (DME), diabetic retinopathy, glaucoma, and cataracts. In the second step, the defective region of multiple eye diseases is segmented using the transfer learning-based DenseUNet model. From the publicly accessible dataset, the suggested model is assessed using several retinal fundus images. Our proposed model for multi-class classification achieves a maximum specificity of 99.73%, a sensitivity of 99.54%, and an accuracy of 99.67%.

Bayesian Optimized Machine Learning Model for Automated Eye Disease Classification from Fundus Images

Eye diseases are defined as disorders or diseases that damage the tissue and related parts of the eyes. They appear in various types and can be either minor, meaning that they do not last long, or permanent blindness. Cataracts, glaucoma, and diabetic retinopathy are all eye illnesses that can cause vision loss if not discovered and treated early on. Automated classification of these diseases from fundus images can empower quicker diagnoses and interventions. Our research aims to create a robust model, BayeSVM500, for eye disease classification to enhance medical technology and improve patient outcomes. In this study, we develop models to classify images accurately. We start by preprocessing fundus images using contrast enhancement, normalization, and resizing. We then leverage several state-of-the-art deep convolutional neural network pre-trained models, including VGG16, VGG19, ResNet50, EfficientNet, and DenseNet, to extract deep features. To reduce feature dimensionality, we employ techniques such as principal component analysis, feature agglomeration, correlation analysis, variance thresholding, and feature importance rankings. Using these refined features, we train various traditional machine learning models as well as ensemble methods. Our best model, named BayeSVM500, is a Support Vector Machine classifier trained on EfficientNet features reduced to 500 dimensions via PCA, achieving 93.65 ± 1.05% accuracy. Bayesian hyperparameter optimization further improved performance to 95.33 ± 0.60%. Through comprehensive feature engineering and model optimization, we demonstrate highly accurate eye disease classification from fundus images, comparable to or superior to previous benchmarks.

Diabetic Retinopathy Features Segmentation without Coding Experience with Computer Vision Models YOLOv8 and YOLOv9.

Computer vision is a powerful tool in medical image analysis, supporting the early detection and classification of eye diseases. Diabetic retinopathy (DR), a severe eye disease secondary to diabetes, accompanies several early signs of eye-threatening conditions, such as microaneurysms (MAs), hemorrhages (HEMOs), and exudates (EXs), which have been widely studied and targeted as objects to be detected by computer vision models. In this work, we tested the performances of the state-of-the-art YOLOv8 and YOLOv9 architectures on DR fundus features segmentation without coding experience or a programming background. We took one hundred DR images from the public MESSIDOR database, manually labelled and prepared them for pixel segmentation, and tested the detection abilities of different model variants. We increased the diversity of the training sample by data augmentation, including tiling, flipping, and rotating the fundus images. The proposed approaches reached an acceptable mean average precision (mAP) in detecting DR lesions such as MA, HEMO, and EX, as well as a hallmark of the posterior pole of the eye, such as the optic disc. We compared our results with related works in the literature involving different neural networks. Our results are promising, but far from being ready for implementation into clinical practice. Accurate lesion detection is mandatory to ensure early and correct diagnoses. Future works will investigate lesion detection further, especially MA segmentation, with improved extraction techniques, image pre-processing, and standardized datasets.

Comparative Study of Classification of Eye Disease Types Using DenseNet and EfficientNetB3

The purpose of this research is to build a classification model that can perform the eye disease identification process so that the diagnosis of eye disease can be known and medical action can be taken as early as possible. This research used a dataset which has a total of 4217 eye image data and had 4 main classes namely cataract, diabetic retinopathy, glaucoma, and normal. With the data distribution of 1038 cataract images, 1098 diabetic retinopathy images, 1007 glaucoma images, and 1074 normal images, which of this data will be divided with a data percentage scheme of 50:10:40, 60:10:30, and 70:10:20, to see the results of which dataset division can produce optimal accuracy. In this study, the classification process will use 2 CNN transfer learning architectures, namely DenseNet, and efficientnetb3, which are both trained using the ImagiNet dataset. The results obtained after completing the testing process on the model built using the DenseNet architecture get optimal accuracy when using data division as much as 60:10:30, which is 78.59% while using the efficientnetb3 architecture optimal accuracy results when using the data division of 70:10:20, which is 95.66%. In research on the classification that had previously been done, it is very rare to find a classification process for eye disease types, therefore, in this study, the classification process will be carried out and provide an overview of the eye disease classification process with the CNN transfer learning method with more optimal accuracy results.

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.

Optimization of the Convolutional Neural Network Method Using Fine-Tuning for Image Classification of Eye Disease

The eye is the most important organ of the human body which functions as the sense of sight. Most people wish they had healthy eyes so they could see clearly about life around them. However, some people experience eye health problems. There are many types of eye diseases ranging from mild to severe. With advances in technology, artificial intelligence can be used to classify eye diseases accurately, one of which is deep learning. Therefore, this study uses the Convolutional Neural Network (CNN) algorithm to classify eye diseases using the VGG16 architecture as a base model and will be combined using a fine-tuning model as an optimization to improve accuracy.

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.