Fundus diseases cause damage to any part of the retina. Untreated fundus diseases can lead to severe vision loss and even blindness. Analyzing optical coherence tomography (OCT) images using deep learning methods can provide early screening and diagn...
Multiple ocular diseases frequently coexist in fundus images, while image quality is highly susceptible to imaging conditions and patient cooperation, often manifesting as blurring, underexposure, and indistinct lesion regions. These challenges signi...
With the growing use of deep learning in medical image analysis, automated classification of fundus images is crucial for the early detection of fundus diseases. However, the complexity of fluorescein fundus angiography (FFA) images poses challenges...
The world faces difficulties in terms of eye care, including treatment, quality of prevention, vision rehabilitation services, and scarcity of trained eye care experts. Early detection and diagnosis of ocular pathologies would enable forestall of vis...
Ultra-widefield fundus image (UFI) has become a crucial tool for ophthalmologists in diagnosing ocular diseases because of its ability to capture a wide field of the retina. Nevertheless, detecting and classifying multiple diseases within this imagin...
Retinal diseases account for a large fraction of global blinding disorders, requiring sophisticated diagnostic tools for early management. In this study, the author proposes a hybrid deep learning framework in the form of AdaptiveSwin-CNN that combin...
Designing computer-aided diagnosis (CAD) systems that can automatically detect ocular diseases (ODs) has become an active research field in the health domain. Although the human eye might have more than one OD simultaneously, most existing systems ar...
Fundus diseases can cause irreversible vision loss in both eyes if not diagnosed and treated immediately. Due to the complexity of fundus diseases, the probability of fundus images containing two or more diseases is extremely high, while existing dee...
At present, multi-disease fundus image classification tasks still have the problems of small data volumes, uneven distributions, and low classification accuracy. In order to solve the problem of large data demand of deep learning models, a multi-dise...
This paper presents a classification model for eye diseases utilizing attention mechanisms to learn features from fundus images and structures. The study focuses on diagnosing glaucoma by extracting retinal vessels and the optic disc from fundus imag...
Glaucoma, an eye disease, occurs due to Retinal damages and it is an ordinary cause of blindness. Most of the available examining procedures are too long and require manual instructions to use them. In this work, we proposed a multi-level deep convol...
The difficulty of classifying retinal fundus images with one or more illnesses present or missing is known as fundus multi-lesion classification. The challenges faced by current approaches include the inability to extract comparable morphological fea...
One of the early manifestations of systemic atherosclerosis, which leads to blood circulation issues, is the enhanced arterial light reflex (EALR). Fundus images are commonly used for regular screening purposes to intervene and assess the severity of...
Glaucoma, a predominant cause of visual impairment on a global scale, poses notable challenges in diagnosis owing to its initially asymptomatic presentation. Early identification is vital to prevent irreversible vision impairment. Cutting-edge deep l...
With recent advancements in machine learning, especially in deep learning, the prediction of eye diseases based on fundus photography using deep convolutional neural networks (DCNNs) has attracted great attention. However, studies focusing on identif...
Automatic classification of arteries and veins (A/V) in fundus images has gained considerable attention from researchers due to its potential to detect vascular abnormalities and facilitate the diagnosis of some systemic diseases. However, the variab...
Background—Diabetes is a rapidly spreading chronic disease that poses a significant risk to individual health as the population grows. This increase is largely attributed to busy lifestyles, unhealthy eating habits, and a lack of awareness abou...
Self-supervised learning has been successful in computer vision, and its application to medical imaging has shown great promise. This study proposes a novel self-supervised learning method for medical image classification, specifically targeting ultr...
Diabetic retinopathy (DR) is the prime cause of blindness in people who suffer from diabetes. Automation of DR diagnosis could help a lot of patients avoid the risk of blindness by identifying the disease and making judgments at an early stage. The m...
Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide. Early detection and effective risk assessment are crucial for implementing preventive measures and improving patient outcomes for CVDs. This work presents a novel approach to...
Effective and transparent medical diagnosis relies on accurate and interpretable classification of medical images across multiple modalities. This paper introduces an explainable multi-modal image analysis framework based on a dual-stream architectur...
Glaucoma is one of the eye diseases stimulated by the fluid pressure that increases in the eyes, damaging the optic nerves and causing partial or complete vision loss. As Glaucoma appears in later stages and it is a slow disease, detailed screening a...
Background/Objectives: The prevailing paradigm in ophthalmic AI involves siloed, single-disease models, which fails to address the complexity of differential diagnosis in clinical practice. This study aimed to develop and validate a unified deep lear...
Ocular disease (OD) represents a complex medical condition affecting humans. OD diagnosis is a challenging process in the current medical system, and blindness may occur if the disease is not detected at its initial phase. Recent studies showed signi...
The aim of this study is to develop a computer-assisted solution for the efficient and effective detection of diabetic retinopathy (DR), a complication of diabetes that can damage the retina and cause vision loss if not treated in a timely manner. Ma...
Accurate segmentation of retinal blood vessels is a key step in the diagnosis of fundus diseases, among which cataracts, glaucoma, and diabetic retinopathy (DR) are the main diseases that cause blindness. Most segmentation methods based on deep convo...
The medical and healthcare domains require automatic diagnosis systems (ADS) for the identification of health problems with technological advancements. Biomedical imaging is one of the techniques used in computer-aided diagnosis systems. Ophthalmolog...
Background: Diabetic retinopathy (DR) is a leading cause of preventable vision impairment in individuals with diabetes. Early detection is essential, yet often hindered by subtle disease progression and reliance on manual expert screening. This study...
Diabetic Retinopathy (DR) is a leading cause of vision impairment globally, underscoring the need for accurate and early diagnosis to prevent disease progression. Although fundus imaging serves as a cornerstone of Computer-Aided Diagnosis (CAD) syste...
Morphological and functional changes in retinal vessels are indicators of a variety of chronic diseases, such as diabetes, stroke, and hypertension. However, without a large number of high-quality annotations, existing deep learning-based medical ima...