The severe progression of Diabetes Mellitus (DM) stands out as one of the most significant concerns for healthcare officials worldwide. Diabetic Retinopathy (DR) is a common complication associated with diabetes, particularly affecting individuals between the ages of 18 and 65. As per the findings of the International Diabetes Federation (IDF) report, 35–60% of individuals suffering from DR possess a diabetes history. DR emerges as a leading cause of worldwide visual impairment. Due to the absence of ophthalmologists worldwide, insufficient health resources, and healthcare services, patients cannot get timely eye screening services. Automated computer-aided detection of DR provides a wide range of potential benefits. In contrast to traditional observer-driven techniques, automatic detection allows for a more objective analysis of numerous images in a shorter time. Moreover, Unsupervised Learning (UL) holds a high potential for image classification in healthcare, particularly regarding explainability and interpretability. Many studies on the detection of DR with both supervised and unsupervised Deep Learning (DL) methodologies are available. Surprisingly, none of the reviews presented thus far have highlighted the potential benefits of both supervised and unsupervised DL methods in Medical Imaging for the detection of DR. After a rigorous selection process, 103 articles were retrieved from four diverse and well-known databases (Web of Science, Scopus, ScienceDirect, and IEEE). This review provides a comprehensive summary of both supervised and unsupervised DL methods applied in DR detection, explaining the significant benefits of both techniques and covering aspects such as datasets, pre-processing, segmentation techniques, and supervised and unsupervised DL methods for detection. The insights from this review will aid academics and researchers in medical imaging to make informed decisions and choose the best practices for DR detection.

Diabetic Retinopathy (DR) is one of the leading causes of blindness and vision loss worldwide. According to the International Diabetes Federation (IDF), approximately one-third of individuals with diabetes, equivalent to 32.2%, are affected by some form of DR. Due to the uneven data distribution, intra-class variance, and a dearth of ophthalmologists, DR diagnosis is considered challenging. In recent years, Convolutional Neural Networks (CNN) and supervised learning techniques have been potentially useful in computer vision applications. However, unsupervised CNN has received less attention. Moreover, it is more manageable to use synthetic images for model training with the recent advancements in graphics. Therefore, the proposed method combines the actual and augmented views using the Deep Convolutional Generative Adversarial Network (DCGAN) algorithm. The generated views are implemented to balance the minority class in the imbalanced dataset. Furthermore, a novel ensemble convolutional neural network algorithm named Different View Ensemble (DVE) that merges the weighted average prediction of CNN, CNN-i, and CNN+i algorithms has been proposed. The proposed algorithm is evaluated on the DDR and EyePACS datasets, and its performance is compared with K-Means, Fuzzy C-Means (FCM), and Autoencoder-based Deep Embedded Clustering Techniques (DEC). The results demonstrate the superiority of the proposed algorithm, achieving an accuracy rate of 97.4%, specificity of 99.6%, and sensitivity of 92.3%. The promising results underscore the potential impact of this methodology in enhancing the accuracy and reliability of automated diagnostic systems in the field of ophthalmology. Notably, the evaluation considers imbalanced data and a DCGAN-balanced dataset, where the proposed approach exhibits even better performance with balanced classes.

According to the International Diabetes Federation (IDF), roughly 33% of individuals affected by diabetes exhibit diagnoses encompassing diverse severity of diabetic retinopathy. In the year 2020, approximately 463 million adults within the age bracket of 20 to 79 were documented as diabetes sufferers on a global scale. Projections suggest a rise to 700 million by 2045. Proposed automated diabetic retinopathy detection methods aim to reduce ophthalmologist workload. The study presents the Robust Fuzzy Local Information K-Means Clustering algorithm, an advanced iteration of the classical K-means clustering approach, integrating localized information parameters tailored to individual clusters. Comparative analysis is conducted between the performance of Robust Fuzzy Local Information K-Means Clustering and Modified Fuzzy C Means clustering, the latter of which incorporates a median adjustment parameter to augment Fuzzy C Means for diabetic retinopathy detection. The results are evaluated on three different datasets: IDRiD, Kaggle, and fundus images collected from Shiva Netralaya Center, India. Achieving a 94.4% accuracy rate and an average execution time of 17.11 seconds, the proposed algorithm aims to adeptly categorize a substantial volume of retinal images, thereby improving performance and meeting the crucial demand for prompt and precise diagnoses in diabetic retinopathy healthcare.