Using Convolutional Neural Networks to Detect and Extract Retinal Blood Vessels in Fundoscopic Images

Diabetes mellitus (DM) is a worldwide major medical problem. Diabetic retinopathy (DR) staging is important for the estimation of DM and the evaluation of associated retinopathy. According to the international clinical diabetic retinopathy & diabetic macular edema disease severity scales, most of the dilated ophthalmoscopy observable findings are associated with retinal blood vessels. In order to objectively and accurately determine the diabetic retinopathy stages, it is essential to automatically detect and extract retinal blood vessels in fundoscopic images. This paper introduces and compares various convolutional neural networks to recognize retinal blood vessels in fundoscopic images. The experimental results demonstrate the effectiveness of the proposed approach.