Adaptive neuro-fuzzy inference system employing wavelet coefficients for detection of ophthalmic arterial disorders

In this study, a new approach based on adaptive neuro-fuzzy inference system (ANFIS) is presented for detection of ophthalmic arterial (OA) disorders. Decision making was performed in two stages: feature extraction using the discrete wavelet transform (DWT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Four types of OA Doppler signals were used as input patterns of the four ANFIS classifiers. To improve diagnostic accuracy, the fifth ANFIS classifier (combining ANFIS) was trained using the outputs of the four ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the impacts of features on the detection of OA disorders were obtained through analysis of the ANFIS. The performance of the ANFIS classifier was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS classifier has potential in detecting the OA disorders.