Exudate detection for diabetic retinopathy with circular Hough transformation and convolutional neural networks

In this study, a combined approach of circular Hough transform and Convolutional Neural Network (CNN) algorithms was proposed for detecting exudates, which is one of the signs of diabetic retinopathy disease. The proposed approach was assessed using DiaretDB0, DiaretDB1 and DrimDB public datasets. This approach consists of visual enhancement with basic pre-processing methods, the segmentation of the OD with the help of circular Hough transformation to ignore the optical disc (OD) regions from the image, and the CNN-based exudate detection system to automatically detect the exudates in the retinal image. In pre-processing and segmentation of the OD region step, adaptive histogram equalization, Canny edge detection algorithm and circular Hough conversion methods are applied respectively to improve retinal images and prevent interference with OD, which is an anatomical region. The images obtained by segmenting and discarding the OD are trained with CNN and subjected to binary classification as exudated and exudate-free image. Then, the method developed with the images not included in the training set was found to have a correct classification ratio of 99.17% in DiaretDB0, 98.53% in DiaretDB1 and 99.18% in DrimDB. This suggests that the results of the proposed approach are more successful than the results obtained using CNN-only or image processing methods alone. Finally, it is seen that the proposed method that applying CNN to the output image of the image processing result, is more successful than the other methods.