Multi-labelled Ocular Disease Diagnosis Enforcing Transfer Learning

Abstract

The leading causes of vision impairment in the working age population today are primarily diseases such as glaucoma, diabetes, etc. Health camps and public health agencies working towards improving eye health among masses engage in activities which require diagnosis on a large scale. This project is aimed at assisting such agencies in effective early diagnosis of these eye diseases by utilizing multi-label CNN-based rapid automated systems to analyze coloured fundus images, thereby mitigating the tedious manual effort associated with clinical diagnosis. Coloured fundus photographs of patients were screened, subjected to various pre-processing techniques - Concatenation, Contrast Limited Adaptive Histogram Equalization and Augmentation; and further classified into 7 labels - Normal, Diabetes, Glaucoma, Cataract, Age related Macular Degeneration, Hypertensive Retinopathy and Pathological Myopia by applying transfer learning using highly effective networks such as VGG-16, InceptionV3 and ResNet50. Performance of each model was evaluated against the Hamming Loss metric. Observed results suggest a significant role of these systems in clinical diagnosis.