The Effect of Weights Initialization on Osteo Arthritis Classification

Standard Back propagation Algorithm (BP) is a widely used in Multilayer Perceptron by the practitioners despite its existence for almost four decades. It is proven to be very successful in diverse applications, such as Osteoarthritis diagnoses. Osteoarthritis diagnoses are one of the most frequent causes of physical disability among adults. this study proposes Osteoarthritis diagnoses classification with improved structures of BP network by proposing acceleration parameters using adaptive learning. The proposed adaptive learning involves two mechanisms: weights initialization and the usage of logarithm activation function to reduce the error rate and convergence time. From the experiments, we found that by selecting appropriate initial weights can lead to feasible results and faster learning for Osteoarthritis diagnoses classification. These are proven by the experiments conducted on the enhanced BP, which is better than a standard BP in terms of faster convergence and less errors generated.