A comprehensive review of retinal disease diagnosis and open access datasets: Fundus and OCT images

blindness in young individuals in both developed and developing nations. Glaucoma damages the visual nerve, causing blindness. Both illnesses have no early signs, making them hard to diagnose. If not treated quickly, these disorders might cause permanent vision loss [3].

Bali and Mansotra, 2023 [1] reviewed various studies that used deep learning models to detect conditions like diabetic retinopathy, age-related macular degeneration, cataracts, and glaucoma. They highlight the effectiveness of these models, emphasising the significance of large datasets for training purposes.

Sengar et al., 2023 [10] developed the EyeDeep-Net, a multi-layer neural network, to train and assess images for detecting different retinal diseases, including AMD, cataract, diabetic retinopathy, Media haze, and amblyopia. The CNN was utilised to identify important characteristics from the input colour fundus image dataset. Multi-class images of fundus were obtained from RFMiD dataset and subsequently used to inform predictive diagnostic judgements based on processed features.

Patil et al., 2023 [5] provided a system that uses deep learning techniques to automatically diagnose five categories of visual disorders using two types of stack ensemble models: ensembles of ensembles and faster ensembles. These categories include cataract, glaucoma, pathological myopia, DR, and eyes with no disease. To increase the robustness and generalization of the models, multiple ocular image datasets were pooled and assessed using metrics such as precision and recall. The ensemble of ensembles framework achieved an accuracy of 88% while the faster ensemble reached 87% accuracy.

Vyas et al., 2022 [9] provided a computer-aided diagnosis system that classifies TBUT frames and assesses the severity and existence of dry eye disease using a modified GoogleNet CNN architecture. Based on the TBUT videos, they classified DED as normal, moderate, or severe, and they have shown a high classification performance with an accuracy rate of 83%. Additionally, a robust 90% correlation was observed with the assessments of ophthalmologists.

Nagamani and Rayachoti, 2024 [7] developed a novel model based on deep learning techniques. The DL-Net aims to enhance the classification and segmentation of retinal diseases by efficiently recording inter-scale characteristics and integrating them using convolutional blocks. The DL-Net model is capable of automatically identifying normal, diabetic macular edoema (DME), choroidal neovascularization (CNV), age-related macular degeneration (AMD), and drusen pictures. The author presented the Modified ResNet-50 methodology and image processing techniques for the classification of multiple OCT images. The authors also proposed an effective detection technique for image segmentation utilising a Bi-LSTM-based deep recurrent convolutional neural network (DRCNN). The revised SqueezeNet model examines the volumetric segmentation of OCT pictures. The suggested framework achieved an accuracy of 99.76%.

Sagvekar et al., 2024 [4] proposed a method for HPLBO “Hybrid hunter-prey ladybug beetle optimization enabled deep learning for DR classification,” The input image was preprocessed using Kalman filter and ROI extraction to reduce noise. The Meyer wavelet, which is a type of wavelet transform, decomposes the image into subbands in order to facilitate subsequent processing. Lesions were segmented through K-Net trained with the Hunter-Prey Ladybug Beetle Optimizer. Veins and arteries were classified from the wavelet-transformed image with an automatic vein and artery classification network enhancement feature extraction phase. Diabetic retinopathy classification is performed using a Deep Maxout network trained with the proposed HPLBO method, achieving 93.6% accuracy.

Thanki, 2023 [17] proposed a smart computer-aided diagnosis system that utilizes machine learning and deep neural networks to analyze colour retinal fundus images in order to classify glaucomatous retinal images. The aim was to enhance the early identification and treatment of glaucoma, with the potential to reduce the risk of blindness. This proposal outline the objective of surpassing the current maximum accuracy of 97% achieved by the existing model for classifying retinal images.

Keenan et al., 2022 [82] Developed a deep learning model called DeepLensNet to automatically diagnose and classify age-related cataracts using anterior segment photographs. After being trained, the model can tell the difference between nuclear sclerosis and other eye conditions by looking at slit-lamp photos and retroillumination photos. The Age-Related Eye Disease Study dataset, which has 18,999 anterior segment images from 1137 eyes, was used in this study to train and test DeepLensNet’s ability to diagnose and classify the severity of cataracts.

Peng et al., 2019 [8] proposed a DeepSeeNet deep learning model to automatically categorize images according to the Simplified Severity Scale of the Age-related Eye Disease Study (AREDS) using bilateral colour fundus pictures. DeepSeeNet utilises a deep CNN with an Inception-v3 architecture for image classification. With 317 layers and over 21 million weights, the model underwent training using the Adam optimizer on powerful CPU and GPU hardware. It was consisted of three sub-networks: Drusen-Net for detecting drusen, Pigment-Net for identifying pigmentary abnormalities, and Late AMD-Net for determining late AMD presence or absence. Training of model involved 58,402 images, and testing included 900 images from follow-up data of 4,549 AREDS participants.

Al-Fahdawi et al., 2024 [83] developed a Fundus-DeepNet model to identify various eye diseases by combining feature representations from set of fundus images. Eight ocular diseases were classified using Discriminative Restricted Boltzmann Machine (DRBM) A series of comprehensive experiments were carried out on the Ophthalmic Image Analysis-Ocular Disease Intelligent Recognition dataset. The experiment results demonstrated F1-scores 88.56%, Kappa scores 88.92%, AUC 99.76%,and final scores of 92.41% in off-site test set,and Fi-scores 89.13%, Kappa scores 88.98%, Auc 99.86%, and Final scores of 92.66% in on -site test set.

Latif et al., 2022 [84] proposed a 2-phase network for localizing the optic disk and diagnosing glaucoma, called the ODGNet Model. The initial phase uses a visual saliency map combined with a shallow CNN to locate the optic disk in fundus images effectively. Pre-trained models based on transfer learning, like VGGNet, ResNet, and AlexNet integrated with saliency maps were employed in second phase for glaucoma diagnosis. These models were assessed using five open access datasets DRIONS-DB, HRF, DR-HAGIS, ORIGA and RIM-ONE. The experimental findings indicate that the ODGNet model, proposed for diagnosis of glaucoma on ORIGA Dataset, is highly predictive and achieves accuracy 95.75%, specificity 94.90%, Sensitivity 94.75%, and AUC 97.85%.

Mistry et al., 2023 [85] described the creation of a computer vision system utilising machine learning and image analysis to identify retinoblastoma, an eye cancer, by automatically analysing fundus photographs. The system integrated innovative approaches in deep learning and segmentation to achieve highly accurate and fast detection of retinoblastoma, enabling early diagnosis compared to conventional methods.

(Uppamma et al., 2023) [27] examined different methodologies employed in the identification and categorization of diabetic retinopathy through the analysis of fundus images. A hybrid machine learning method that combines various learning strategies and feature extraction techniques was proposed. Additionally, a Deep Transfer Learning method was employed, which utilises Inception-v3 to detect different layers of Convolutional Neural Networks. Furthermore, a Toboggan segmentation and multiagent procedure was utilised for damaged retinal vascular segmentation. Lastly, image quality was enhanced using Gaussian and modified Kirsch filters.

Shaikh et al., 2022 [86] presented a machine learning model developed to aid in the quicker and more efficient detection of eye diseases. The researchers trained a CNN model on a dataset of 372 images, categorized into normal eyes, glaucoma, and retinopathy. Images were preprocessed and converted to grayscale for enhanced feature extraction. The model achieved high accuracy rates: 99.01% for normal images, 99.99% for glaucoma, and 98.99% for retinopathy.

Malik et al., 2019 [87] proposed a framework that focuses on gathering structured diagnostic data in a globally recognised format to predict various eye diseases listed in ICD-10. It took into account all potential symptoms of both the front and back portion of eye. Medical experts entered a total of fifty-two diagnoses in the software. A two-stage diagnostic method was created to exclude patient information concerning refractive error prior to analysis by machine-learning algorithms. Four different classification techniques were evaluated, specifically the decision tree, random forest, naïve Bayes, and ANN. The random forest algorithm outperformed the artificial neural network, although it did have a slightly longer execution time compared to the decision tree algorithm.

Sait, 2023 [11] developed a DL-based EDC (Eye Disease classification) model to identify eye diseases like DR, glaucoma and cataracts early from complicated retinal pictures. In their approach, firstly, Image preprocessing was done using denoising autoencoders, then features were extracted using SSD approach. The selection of critical features was conducted using the Whale Optimisation Algorithm (WOA) with the Levy Flight and Wavelet mutation strategies. The ShuffleNet V2 model’s ideal parameters for categorising eye diseases were found using the Adam Optimizer. The suggested model exhibited substantial improvement, achieving an impressive accuracy rate of 99.1% and a sensitivity rate of 98.9% when evaluated on the ODIR dataset. Similarly, when tested on the EDC dataset, the model achieved an accuracy rate of 99.4% and a sensitivity rate of 98.7%.

Marouf et al., 2022 [12] Introduced an effective methodology for accurate prediction of five distinct ocular conditions: cataracts, primary congenital glaucoma, acute angle-closure glaucoma, exophthalmos, and ocular hypertension. Moreover, offered a comprehensive evaluation of nine machine learning techniques including Decision Tree, Random Forest, Naive Bayes, AdaBoost,Logistic Regression, kNN, Bagging, Boosting and SVM. In terms of performance, SVM emerged as the top performer with an impressive accuracy rate of 99.11% in a 10-fold cross-validation process. Logistic Regression also achieved noteworthy accuracy at 98.58% using an 80:20 split ratio.

Lee et al., 2020 [88] introduced a new technique for generating retinal images that can facilitate accurate diagnoses after retinal exams. This proposed approach ensures the capability to distinguish vessel areas in images, which are critical for examinations, even when working with low-resolution retinal images. This was accomplished by selecting the identical high-resolution images from a given dataset and isolating their high-resolution vessel segments. These segments were then combined with the low-resolution ocular image to create a distinct vessel image, aiding in making accurate diagnoses.

Winston et al., 2019 [89] proposed two modified SOM self organising map networks that were trained with the extracted feature vectors. The training process of the conventional SOM was enhanced by using sarm optimization technique.Their main objective was to speed up the learning time and improving the performance of the iris classification system.

Prasad et al., 2019 [3] proposed a deep neural network framework that utilizes Convolutional Neural Networks to automatically diagnose the occurrence of DR and glaucoma at their initial phases from fundus images sourced from Kaggle and Medimrg. The developed model can identify the diseases with 80% accuracy.

Zhu et al., 2024 [6] examined various deep learning architectures like CNN models, transfer learning strategies, and the utilization of datasets like APTOS 2019 and EyePACS for diabetic retinopathy classification. According to their findings the CNN models, particularly those based on the ResNet and VGGNet frameworks, are the most popular for Diabetic Retinopathy categorisation due to their depth, which can range from tens to hundreds of layers, resulting in excellent classification outcomes. Table 3 Shows the Summary of Deep Learning and Machine Learning Models for classification of multiple ocular diseases.