A Automatic recognition of diabetic retinal degeneration with machine learning using Fondus images

Lahari Pulibanti — Fri, 10 May 2024 00:00:00 +0000

Automatic recognition of diabetic retinal degeneration using machine learning techniques, particularly leveraging Fundus images, has emerged as a crucial area in medical image analysis. Diabetic retinal degeneration is a leading cause of vision impairment and blindness among diabetic patients, making early detection and intervention imperative. This abstract presents an overview of recent advancements in utilizing machine learning algorithms for the automated identification and classification of diabetic retinal degeneration from Fundus images.

The proposed approach involves preprocessing Fundus images to enhance quality and extract relevant features, followed by the application of various machine learning models for classification tasks. Feature extraction techniques such as texture analysis, shape descriptors, and pixel intensity statistics are employed to capture distinct patterns indicative of diabetic retinal degeneration. Subsequently, these features are fed into machine learning classifiers such as support vector machines (SVM), convolutional neural networks (CNN), and random forests (RF) to categorize Fundus images into different stages of retinal degeneration.

Key challenges in this domain include the large variability in Fundus image quality, the complexity of diabetic retinal degeneration patterns, and the need for robust algorithms capable of handling diverse datasets. Furthermore, issues related to interpretability, scalability, and clinical integration of automated systems remain pertinent.

Despite these challenges, recent studies have demonstrated promising results, achieving high accuracy and sensitivity in diabetic retinal degeneration detection. By leveraging machine learning techniques, healthcare providers can potentially enhance the efficiency and accuracy of diagnosis, leading to timely interventions and improved patient outcomes. Continued research efforts are necessary to address existing limitations and facilitate the translation of these automated systems into clinical practice.

International Journal of Engineering Research and Development

A Automatic recognition of diabetic retinal degeneration with machine learning using Fondus images