Cataract detection and visualization based on multi-scale deep features by RINet tuned with cyclic learning rate hyperparameter

The most common reason worldwide for blindness is cataract, the clouding of the lens where vision becomes increasingly blurry. Early detection of cataract is crucial for a better prognosis. This can be achieved with improved automated cataract detection and localization of the affected regions in the fundus images. This work proposes a supervised miniature U-Net (SMi-UNet) for feature extraction integrated with a convolutional neural network (RINet) explicitly designed for fundus images. The dataset used has been self-curated by collecting cataract images from multiple repositories. The proposed SMi-UNet allows a deeper network with significantly reduced parameters than conventional U-Net. The proposed RINet utilizes the features extracted by SMi-UNet for discriminating between a cataract and a normal fundus image. Further, the performance of the RINet is optimized using a cyclic learning rate (CLR) hyperparameter. CLR eliminates the need to find the best value of the learning rate and improves accuracy in a minimum number of epochs, making it suitable for edge devices. Further, to localize the prominent regions in the cataract images, colored heatmap techniques are applied at the last convolutional layer. These maps help visualize the affected areas with a hotter color. The exceptional performance of the proposed technique in cataract detection and its localization has been established by quantitative and qualitative data, demonstrating that it can be a valuable tool for early cataract detection. The obtained results were validated by an expert. The proposed RINet attains a classification accuracy of 96% and 93% with and without CLR respectively.