Digital image classification plays a significant role in the early detection and analysis of medical conditions. Traditionally, diagnosis is performed manually by ophthalmologists through examination of retinal fundus images. However, this process is time-consuming, requires expert knowledge, and may sometimes lead to errors due to human limitations. In contrast, automated digital image classification systems provide a faster, more consistent, and cost-effective solution by analyzing large volumes of medical images efficiently. This work focuses on the application of digital image classification techniques for identifying different stages of diabetic retinopathy. Additionally, different image preprocessing, feature extraction, and classification methods are discussed. The study also summarizes the key image features commonly used in previous research for accurate classification of retinal images into different disease categories.
Vishwakarma et al. (Wed,) studied this question.