Gabor Wavelet Based Diabetic Retinopathy Detection Using Deep Learning

Diabetic Retinopathy (DR) is the disease that causes blindness when it reaches to the proliferative stage. There are four stages in DR namely: NO DR, Mild DR, Severe DR and Proliferative DR. The detection of diabetic retinopathy in the early stages can prevent sight loss in a notable amount of the population worldwide. The earliest signs of diabetic retinopathy are hemorrhages and hard exudates which are red and yellow lesions on the retina of the eye. Diagnosis of the DR is performed using retinal image analysis. Manual Analysis of these images to decide the presence of the DR is somewhat slow and costly. To ease the job of a medical consultant, we can take the images of the retina and feed them to the trained machine learning model and get the results whether the person has diabetic retinopathy or not. In this thesis, we propose a system to detect and classify diabetic retinopathy using the Gabor filter-based CNN with fusion. Proposed model fuses the decisions generated by Gabor-based CNN and traditional CNN pipelines. A preprocessing stage is employed to normalize images before feeding them into the CNN architecture. Preprocessing includes cropping and image resizing. A pre-trained CNN model VGG16, is selected as a deep machine learning architecture throughout the thesis. Decision fusion approaches including Sum Rule and Product Rule are employed throughout the fusion process. The recall, specificity, f1score, precision, and accuracy of the models have been studied and generated results are compared with each other and methods in the literature. The receiver characteristics curve (ROC) and Area under the curve (AUC) metrics are also measured to evaluate the classification performance. Messidor retina image database have been used to measure the performance of the iv proposed system. The results show that the proposed system to detect and classify diabetic retinopathy using the Gabor filter-based CNN with fusion generates higher performance over the state-of-the-art alternative methods in the literature. Keywords: Convolutional Neural Networks, Deep Learning, Machine Learning, Gabor Filters, Data Fusion.