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Application of Multi Layer (Perceptron) Artificial Neural Network in the Diagnosis System: A Systematic Review

Basic hardware comprehension of an artificial neural network (ANN), to a major scale depends on the proficient realization of a distinct neuron. For hardware execution of NNs, mostly FPGA-designed reconfigurable computing systems are favorable. FPGA comprehension of ANNs through a huge amount of neurons is mainly an exigent assignment. This work converses the reviews on various research articles of neural networks whose concerns focused in execution of more than one input neuron and multilayer with or without linearity property by using FPGA. An execution technique through reserve substitution is projected to adjust signed decimal facts. A detailed review of many research papers have been done for the proposed work. The proposed paper involves a Multi Layer Perceptron with a Back Propagation learning algorithm to identify a prototype for the diagnosis. In this paper, a brief introduction about artificial neural network used nowadays for diagnosis of disease is given.