Retinal fundus diseases diagnosis using image mining

In recent years, the exponential growth of both structured and unstructured data has highlighted the importance of efficient knowledge discovery techniques. Among these, image mining and data mining play pivotal roles in extracting meaningful patterns and insights from different forms of information. At the same time, data mining focuses on structured datasets such as databases and transaction records, while image mining deals with unstructured visual content, requiring advanced feature extraction, pattern recognition, and machine learning methods. This paper presents a comparative analysis of image mining and data mining techniques, emphasising their methodologies, applications, and challenges. The comparison explores key dimensions, including data preprocessing, feature selection, algorithmic approaches, and application domains such as healthcare, security, business intelligence, and multimedia. Experimental results show clustering accuracy of 87% for data mining and image classification accuracy of 92% for image mining, highlighting the effectiveness of specialised approaches. The study highlights both the similarities and differences in knowledge discovery processes, demonstrating how integrating image mining and data mining can enhance decision-making in diverse fields. The findings provide a comprehensive understanding of their complementary roles, offering valuable insights for researchers and practitioners aiming to develop hybrid approaches to knowledge discovery.

Classifying retinal diseases is a complex problem because the early problematic areas of retinal disorders are quite small and conservative. In recent years, Transformer architectures have been successfully applied to solve various retinal related health problems. Age-related macular degeneration (AMD) and diabetic macular edema (DME), two prevalent retinal diseases, can cause partial or total blindness. Diseases therefore require an early and accurate detection. In this study, we proposed Vision Transformer (ViT), Tokens-To-Token Vision Transformer (T2T-ViT) and Mobile Vision Transformer (Mobile-ViT) algorithms to detect choroidal neovascularization (CNV), drusen, and diabetic macular edema (DME), and normal using optical coherence tomography (OCT) images. The predictive accuracies of ViT, T2T-ViT and Mobile-ViT achieved on the dataset for the classification of OCT images are 95.14%, 96.07% and 99.17% respectively. Experimental results obtained from ViT approaches showed that Mobile-ViT have superior performance with regard to classification accuracy in comparison with the others. Overall, it has been observed that ViT architectures have the capacity to classify with high accuracy in the diagnosis of retinal diseases.

The image mining technique deals with the extraction of implicit knowledge and image with data relationship or other patterns not explicitly stored in the images. It is an extension of data mining to image domain. The main objective of this paper is to apply image mining in the domain such as breast mammograms to classify and detect the cancerous tissue. Mammogram image can be classified into normal, benign, and malignant class. Total of 26 features including histogram intensity features and gray-level co-occurrence matrix features are extracted from mammogram images. A hybrid approach of feature selection is proposed, which approximately reduces 75% of the features, and new decision tree is used for classification. The most interesting one is that branch and bound algorithm that is used for feature selection provides the best optimal features and no where it is applied or used for gray-level co-occurrence matrix feature selection from mammogram. Experiments have been taken for a data set of 300 images taken from MIAS of different types with the aim of improving the accuracy by generating minimum number of rules to cover more patterns. The accuracy obtained by this method is approximately 97.7%, which is highly encouraging.

The extraction of retinal vessels plays an important role in the diagnosis and study of retinal diseases, such as Age-related Macular Degeneration (AMD), Diabetic Retinopathy, Retinopathy of Prematurity (ROP). Vessel diameters, tortuosity, branch lengths, angles, and bifurcations are essential to diagnosing these diseases. However, this is a challenging task due to high noise levels, the low contrast of thin vessels to the background, non-uniform illumination, and the central light reflex. Our goal here is to develop a framework to accurately segment the retinal vessels as a preprocessing step for the feature extraction of the vessels towards the future disease diagnosis. In this paper, we present a principal curve based retinal vessel segmentation approach to achieve this goal. We first use the isotropic Gaussian kernel Frangi filter to enhance the retinal vessels and measure the diameters of them. A multiscale principal curve projection and tracing algorithm is then proposed to identify the centerlines of the vessels in the output image of the Franfi filter using the underlying kernel smoothing interpolation of the intensities. The estimated vessel radius from the Frangi filter are used as the bandwidth of the kernel interpolation in the principal curve projection and tracing step. The vessel features toward diagnosing and analyzing the diseases can be extracted from our segmentation results. The presented approach is implemented on a publicly available DRIVE database [16].

Artificial intelligence (AI) has revolutionized optical coherence tomography (OCT) imaging for the better detection of retinal diseases like diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma. Here, bibliometric analysis of AI in OCT is presented. We conducted a systematic search in the Web of Science Core Collection (WoSCC) database up to January 22, 2025 with the help of AI and OCT-related terms. The journal articles and reviews on the topic were examined based on publication trends, author productivity, institution affiliation, and keyword co-occurrence. Visualization was performed through VOSviewer software (1.6.20). A total of 765 articles were analyzed with an increasing trend in publications, led by the USA (30.7%), China (23.3%), and the UK (11.1%) in research production. The most cited institution was the Medical University of Vienna (9.0%), and the most productive journal was Scientific Reports (7.3%). The most common keywords were "Optical Coherence Tomography", "Deep Learning", and "Diabetic Retinopathy" with frequency of 469, 297, 221, respectively. Our review of AI-enhanced OCT for retinal diseases was both comprehensive and systematic, identifying key trends and current research hotspots. As research productivity in this field continues to grow, the focus is increasingly shifting toward developing more accurate AI-driven imaging techniques to improve the diagnosis of various retinal diseases. Future work should prioritize algorithm validation and clinical implementation to facilitate widespread adoption

AngioPlex™ optical coherence tomography (OCT) is a new approach to diagnostic imaging in retinal disease. This technology uses amplitude and phase aspects of the OCT signal and novel algorithms to enable highly detailed visualisation of the retinal microvasculature. This system detects capillary flow rather than the presence of an injected dye. Unlike fluorescein angiography (FA), AngioPlex OCT imaging can discriminate capillaries at the superficial and deep retina and can also generate a colour map of the retinal vessels providing unparalleled views of diseased capillaries and net structures. Retinal and choroidal pathologies that are amenable to AngioPlex OCT examination include diabetic retinopathy (DR), vein occlusions, age-related macular degeneration and pathological myopia. In addition, AngioPlex OCT can be used to explore the vasculature of the optic nerve. These applications have considerable potential for improved retinal and choroidal disease diagnosis, but further experience and debate are needed to fully interpret the images. AngioPlex OCT is rapid, non-invasive and improves the patient experience and has been granted US Food and Drug Administration (FDA) 510(k) clearance for clinical use. The convenience and the range of retinal disease applications may encourage widespread adoption of the technology. AngioPlex OCT may therefore become a standard initial examination step for patients with retinal disease prior to and during treatment and may also indicate where further investigations including FA or indocyanine green are necessary.

Vision impairment is becoming a major health concern, especially in elderly people. While in the medical field, manually detecting ocular pathology has significant difficulty. Therefore, deep learning diagnostic techniques are widely used for identifying eye diseases and play a crucial role in diagnosing vision-related problems. Examination of fundoscopy allows for analyzing eyes for diagnosis of eye diseases, including Diabetic retinopathy (DR), Cataracts, Glaucoma, Age‑related macular degeneration, Pathologic Myopia, and more. In this paper, we propose a concise review of introducing most of the DL, hybrid, and ensemble models utilized for the purpose of identification and classification of eye diseases. Various datasets, feature extraction techniques, and metrics for performance evaluation are discussed. The chosen papers come from conferences and scholarly publications published from 2019 to 2024. We evaluate the performance of chosen researches using different datasets, the most common ones include ocular disease intelligent recognition, Indian DR image dataset, EyePACS, methods to evaluate segmentation and indexing techniques in the field of retinal ophthalmology, methods to evaluate segmentation and indexing techniques in the field of retinal ophthalmology-version 2, DIARETDB, Structured analysis of the retina, high-resolution fundus, digital retinal images for vessel extraction, online retinal fundus image dataset for Gl analysis and research, retinal fundus multi-disease image dataset and Kaggle datasets. The detection studies that have been reviewed show that the accuracy of these approaches varies between 73% and 99%, the sensitivity ranges from 69% to 99% and precision is between 89% and 99%. The results show that great accuracy is consistently achieved with DL algorithms compared to traditional Machine learning approaches. However, there are still some challenges and limitations remaining including excessive resource consumption and over-fitting due to dataset size and diversity issues. This review offers useful insight for researchers and healthcare professionals to comprehend AI technologies properly for the detection, classification, and diagnosis of retinal diseases. We succinctly summarize the methodologies of all the chosen studies and focus on the elements that define the aim of the studies.

In this era, deep learning-based medical image analysis has become a reliable source in assisting medical practitioners for various retinal disease diagnosis like hypertension, diabetic retinopathy (DR), arteriosclerosis glaucoma, and macular edema etc. Among these retinal diseases, DR can lead to vision detachment in diabetic patients which cause swelling of these retinal blood vessels or even can create new vessels. This creation or the new vessels and swelling can be analyzed as biomarker for screening and analysis of DR. Deep learning-based semantic segmentation of these vessels can be an effective tool to detect changes in retinal vasculature for diagnostic purposes. This segmentation task becomes challenging because of the low-quality retinal images with different image acquisition conditions, and intensity variations. Existing retinal blood vessels segmentation methods require a large number of trainable parameters for training of their networks. This paper introduces a novel Dense Aggregation Vessel Segmentation Network (DAVS-Net), which can achieve high segmentation performance with only a few trainable parameters. For faster convergence, this network uses an encoder-decoder framework in which edge information is transferred from the first layers of the encoder to the last layer of the decoder. Performance of the proposed network is evaluated on publicly available retinal blood vessels datasets of DRIVE, CHASE_DB1, and STARE. Proposed method achieved state-of-the-art segmentation accuracy using a few number of trainable parameters.

The use of intravitreal corticosteroids in the management of macular oedema has recently gained widespread acceptance. New long-acting steroid preparations and methods of delivery have facilitated the use of these new modalities. This review describes the various types of macular oedema for which this therapeutic option is used and the results.

Highlights from the Diabetes UK Professional Conference 2017

Retinal Image Enhancement and Eye Disease Identification

The pig has been used as a large animal model in biomedical research for many years and its use continues to increase because induced mutations phenocopy several inherited human diseases. In addition, they are continuous breeders, can be propagated by artificial insemination, have large litter sizes (on the order of mice), and can be genetically manipulated using all of the techniques that are currently available in mice. The pioneering work of Petters and colleagues set the stage for the use of the pig as a model of inherited retinal disease. In the last 10 years, the pig has become a model of choice where specific disease-causing mutations that are not phenocopied in rodents need to be studied and therapeutic approaches explored. The pig is not only used for retinal eye disease but also for the study of the cornea and lens. This review attempts to show how broad the use of the pig has become and how it has contributed to the assessment of treatments for eye disease. In the last 10 years, there have been several reviews that included the use of the pig in biomedical research (see body of the review) that included information about retinal disease. None directly discuss the use of the pig as an animal model for retinal diseases, including inherited diseases, where a single genetic mutation has been identified or for multifactorial diseases such as glaucoma and diabetic retinopathy. Although the pig is used to explore diseases of the cornea and lens, this review focuses on how and why the pig, as a large animal model, is useful for research in neural retinal disease and its treatment.

Diabetic Mellitus is the most familiar disease around the globe. Long prevalence of diabetes causes several problems related to health. The most common issue is Diabetic Retinopathy (DR). Diabetic retinopathy is a situation in which the vessels inside the retina are vandalized, leaking harmful substances and fluids in the surrounding tissue resulting in hemorrhages, micro aneurysms in the eye and further into partial or complete vision loss. This disease if treated in the early stage can help to prevent vision loss, but since it takes time for diagnosis and there is a shortage of ophthalmologists' patients suffer vision loss even before diagnosis. Hence, early detection of DR may help in reducing the problem. Therefore, in this paper we investigate various approaches to understand the process of detecting Diabetic Retinopathy as accurately as possible and classifying them into different grades of treatable DR (NPDR) namely LO, L1 DR, L2 DR and Proliferate DR (PDR) using Deep Learning and Image Processing techniques also making some improvisations on the same to enhance the capability of other existing systems.

The review paper describes the application of image processing techniques for automatic detection of eye diseases. Large percentages of people suffer from eye diseases in rural and semi urban areas in India as well as world over. Image processing techniques greatly help diagnosing various eye diseases. Current diagnosis of retinal disease relies strongly upon optical imaging methods due to the photon sensing nature of the eye over a wide band of wavelengths. The key image processing elements to detect eye diseases include image registration, fusion, segmentation, feature extraction, enhancement, pattern matching, image classification, analysis and statistical measurements. In developing and under developing countries large number of people are suffering from ophthalmic diseases like Glaucoma, Age related Macular Degeneration (AMD), Diabetic Retinopathy, Diabetic hypertension. A large deficit of ophthalmologists exists in these regions. Year after year the number of medical assistants is decreasing, while demand for healthcare is increasing and expected to touch 40% by 2020. Low cost instrumentation with internet and mobile enabled network connectivity with above techniques can help patients in rural and semi urban areas to access well equipped and sophisticated hospitals in cities [1].

Diabetic retinopathy (DR) is a chronic microvascular complication of diabetes and remains one of the leading causes of preventable blindness worldwide. For DR, early detection and accurate stage classification through fundus image analysis are critical for timely intervention and effective treatment planning. In this work, we present a novel hybrid classification pipeline for DR screening that integrates transformer-based vision models with optimization-guided feature selection. The architecture leverages a dual-stream design, combining the global feature extraction capability of a refined vision transformer (RViT) with the local feature capturing the strength of an adaptive MobileNetV3 (AdaMobileNetV3). This design enhances the modelling of retinal lesions, such as microaneurysms, hemorrhages, and exudates, at a local scale while concurrently capturing global contextual information. A two-stage feature selection strategy is employed, initially removing highly correlated features, followed by selecting the most discriminative subset using the weighted goose optimization algorithm (WGOA). The proposed method is evaluated on two DR datasets featuring varying class distributions, including imbalanced and multi-label scenarios. The proposed architecture outperforms baseline methods (RViT-AdaMobileNetV3 and RViT-AdaMobileNetV3+GA), achieving macro F1-scores of 0.9800 on Dataset A and 0.9764 on Dataset B, thereby demonstrating its robustness, adaptability, and translational potential for cross-dataset DR classification tasks.