Tumour Detection using Convolutional Neural Network on a Lightweight Multi-Core Device

Abstract

Convolutional neural networks (CNN) have been the main driving force behind image classification and it is widely used. Large amounts of processing power and computation complexity is required to mimic our human brain as in the image classification. Such complexity may result in large bulky systems. A lack of such, while possible, may result in a rather limited use case and as such constrained functional implementation. One solution is to explore the use of Multicore System on Chips (MCSoC). CNN, however, were commonly built on Graphics Processing Units (GPU) based machine. In this paper, we reduce the overall size of a CNN while retaining a satisfactory level of accuracy so that it is better suited to be deployed in an MCSoC environment. We trained a CNN model that was validated on detecting malignant tumor cells. The results show significant boost in functionality in the form of faster inference times and smaller model parameter sizes, deploying neural networks in an environment that would have otherwise seemed less practical. Efficient inference networks on lightweight systems can serve as an inexpensive and physically small alternative to existing Artificial Intelligence (AI) systems that are generally costly, bulky and power hungry.