Deep learning architecture based on segmented fundus image features for classification of diabetic retinopathy

Diabetic retinopathy is ophthalmological distress, diabetic patients suffer due to clots, lesions, or haemorrhage formation in the light-sensitive region of the retina. Blocking of vessels leads, due to the increase of blood sugar leads to the formation of new vessel growth, which gives rise to mesh-like structures. Assessing the branching retinal vasculature is an important aspect for ophthalmologists for efficient diagnosis. The fundus scans of the eye are first subjected to pre-processing, followed by segmentation. To extract the branching blood vessels, the technique of maximal principal curvature has been applied, which utilizes the maximum Eigenvalues of the Hessian matrix. Adaptive histogram equalization and the morphological opening, are performed post to that, to enhance and eliminate falsely segmented regions. The proliferation of optical nerves was observed much greater in diabetic or affected patients than in healthy ones. We have used a convolution neural network (CNN) to train the classifier for performing classification. The CNN, constructed for classification, comprises a combination of squeeze and excitation and bottleneck layers, one for each class, and a convolution and pooling layer architecture for classification between the two classes. For the performance evaluation of the proposed algorithm, we use the dataset DIARETDB1 (standard Diabetic Retinopathy Dataset) and the dataset provided by a medical institution, comprised of fundus scans of both affected and normal retinas. Experimental results show that the proposed algorithm provides improved results, when compared to traditional schemes. The model yielded an accuracy of 98.7 % and a precision of 97.2 % while evaluated on the DIARETDB1 dataset.