Performance Enhanced Hybrid Artificial Neural Network for Abnormal Retinal Image Classification

Abstract

Artificial Neural Networks (ANN) is becoming increasingly important in the medical field for diagnostic applications. The popularity of ANN is mainly due to the high accuracy and the nominal convergence rate. But, the major drawback is that these characteristic features are not simultaneously available in the same network. While supervised neural networks are highly accurate, the requirement for convergence time is high. On the other hand, unsupervised neural networks are sufficiently faster but less accurate. This problem is tackled in this work by proposing a Modified Neural Network (MNN) which possesses the features of both the supervised neural network and the unsupervised neural network. The applicability of this network is explored in the context of abnormal retinal image classification. Images from four abnormal categories such as Non-Proliferative Diabetic Retinopathy (NPDR), Choroidal Neo-Vascularization Membrane (CNVM), Central Serous Retinopathy (CSR) and Central Retinal Vein Occlusion (CRVO) are used in this work. Suitable features are extracted from these images and further used for the training and testing process of the proposed ANN. Experimental results are analyzed in terms of classification accuracy and convergence rate. The experimental results are also compared with the results of conventional networks such as Back Propagation Network (BPN) and the Kohonen Network (KN). The results of the proposed modified ANN are promising in terms of the performance measures.