Identification of Diabetic Retinopathy Stages Using Modified DWT and Artificial Neural Network

This book focuses on the automatic detection of diabetic retinopathy (DR) through digital image processing techniques. Diabetic retinopathy is a major cause of vision loss among diabetic patients, and early detection can significantly reduce the risk of blindness. However, the shortage of eye specialists and the lack of accessible diagnostic tools make timely diagnosis challenging, particularly in developing countries. This book proposes a low-cost, efficient method using mobile phone-based retinal imaging to detect DR, providing an alternative to expensive fundus cameras. By applying digital image processing techniques, it aims to simplify the identification process, enabling early diagnosis and better patient outcomes.

Diabetic retinopathy is a leading cause of blindness, and its early detection is crucial in preventing vision loss. This book addresses the challenges in diagnosing diabetic retinopathy, particularly in resource-limited settings, and proposes an automated solution using digital image processing. By leveraging mobile phone cameras and inexpensive condensing lenses to capture retinal images, the book presents a cost-effective method for DR detection. The research aims to aid ophthalmologists by streamlining the diagnosis process, thus enabling them to focus on treatment. The goal is to increase awareness, facilitate early diagnosis, and ultimately reduce the incidence of blindness due to diabetic retinopathy.

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