Role of Machine and Deep Learning Techniques in Diabetic Retinopathy Detection

Abstract

Machine learning (ML) and deep learning (DL) techniques play a significant role in diabetic retinopathy (DR) detection via grading the severity levels or segmenting the retinal lesions. High sugar levels in the blood due to diabetes causes DR, a leading cause of blindness. Manual detection or grading of the DR requires ophthalmologists' expertise and consumes time prone to human errors. Therefore, using fundus images, the ML and DL algorithms help automatic DR detection. The fundus imaging analysis helps the early DR detection, controlling, and treatment evaluation of DR conditions. Knowing the fundus image analysis requires a strong knowledge of the system and ML and DL functionalities in computer vision. DL in fundus imaging is a rapidly expanding research area. This chapter presents the fundus images, DR, and its severity levels. Also, this chapter explains the performance analysis of the various ML DL-based DR detection techniques. Finally, the role of ML and DL techniques in DR detection or severity grading is discussed.