Deep neural network based robust computer-aided cataract diagnosis system using fundus retinal images. (September 2021) - Pratap, Turimerla; Kokil, Priyanka
Abstract: Cataract is the cloudiness present in the eye lens due to denaturation of active protein cells. Cataract affects the quality-of-life and thereby troubling the daily routine activities. Early diagnosis and treatment may reduce the vision loss and delays the cataract progression. To diagnose large-screen population, the computer-aided cataract diagnosis (CACD) system using fundus retinal (FR) images is required. In this paper, a CACD system using FR images is proposed to achieve better diagnostic accuracy. It is perceived that the performance of existing CACD systems is poor against noisy input FR images. However, the distortion such as noise is unavoidable in input images due to complex processes involved in the image acquisition. Hence, it is required to consider the effect of noise in the design of CACD systems. So the proposed CACD system includes this issue in the design and provides the robust performance. In the presented CACD system, the features are extracted using combined feature extraction (CFE) technique using two independently fine-tuned deep convolutional neural networks. The noise level estimation (NLE)-based classification is adopted in the classification stage. In NLE-based classification, a set of multi-class support vector machine (SVM) classifiers, which are trained independently at noise levels from 0 to 25 are considered. Finally, the features extracted using CFE are then mapped to a specific multi-class SVM classifier based on noise level present in an input FR image. From the experimental results, it is observed that the proposed system exhibits superior performance than existing CACD systems under noisy conditions. Highlights: A deep neural network based robust cataract diagnosis system is proposed. The proposed cataract diagnosis system is robust against noisy environment. A combined feature extraction technique is used to enhance the robust performance. Noise level estimation-based classifier is presented for optimum performance. The proposed system is robust and better when compared with existing systems.