Digital implementation of a spiking convolutional neural network for tumor detection

Abstract

The structural variation of the brain tissue creates challenges for detection of tumors in MRI images. In this paper, an architecture for spiking convolutional neural networks (SCNNs) is implemented in an embedded system and their potential is evaluated in terms of hardware utilization and power consumption in complex applications such as tumor detection. Accordingly, the structure of the proposed SCNN is implemented on a field-programmable gate array (FPGA) using fixed point arithmetic. To evaluate the speed, accuracy and flexibility of the proposed SCNN, Izhikevich neuron model is used with the spike-timing-dependent plasticity (STDP) learning rule. The suggested neural network is explored for digital implementation possibility and costs. Results of the hardware synthesis and digital implementation are presented on an FPGA.