Deep learning neural network with transferlearning for liver cancer classification

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Abstract Hepatocellular carcinoma (HCC), “primary” cancer of the liver is the fourth most common cause of cancer related death worldwide. However,it can be reduced by early detection and diagnosis. With increasing use of Computed topography (CT) and Magnetic resonance (MR) imaging for diagnosis, traditional classification cancer manually is a difficult and time consuming task showing limitations in large and diverse datasets. Computer Aided Diagnosis (CAD) can play a key role in the early detection and diagnosis of liver cancer. Therefore the main objective of this work is to detect the liver cancer accurately using deep learning. We propose a novel CAD framework using convolution neural networks and transfer learning (pre-trained VGG-16 and MonileNet-V1 model). The classification accuracy of HCC reaches 96%. The system consists of three stages: (i) data collection , (ii) Data processing and (iii) Data Analysis.

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