Liver Cancer Trait Detection and Classification Through Machine Learning on Smart Mobile Devices

Machine learning techniques have provided a technological evolution in medicine and especially in the field of medical imaging. The aim of this study is to firstly compare multiple transfer learning architecture models such as MobilleNetVn (n = 1, 2), InceptionVn (n = 1, 2, 3, 4), InceptionResNet, VGG16 and NasNetMobile and provide a final performance estimation, using a variety of metrics, secondly, propose an efficient and accurate classifier for liver cancer trait detection and prediction, and thirdly, develop a mobile application which uses the proposed model to classify liver cancer traits into various categories in real-time. Magnetic Resonance Images (MRI) of mouse liver cancer of different origin were used as input datasets for our experiments. However, the required memory by the deployed Convolutional Neural Network (CNN) models on smart mobile devices or embedded systems for real time applications, is an issue to be addressed. Here, all the baseline pre-trained CNN models on the ImageNet dataset were trained on a dataset of MRI images of mice of different genetic background with genetically- or chemically- induced hepatocellular cancer. We present and compare all main metric values for each model such as accuracy, cross entropy, f-score, confusion matrix for various types of classification. Data analysis verifies that the proposed optimized architecture model for this task of liver cancer trait classification and prediction, the MV1-LCCP, shows a suitable performance in terms of memory utilization and accuracy, suitable to be deployed in the mobile Android application, which is also developed and presented in this paper.