Convolutional Neural Networks based Liver Tumor Classification

Abstract

The most common and widespread disease among today's population is liver disease, which is caused by excessive alcohol consumption, polluted gas produced by various chemical factories, drugs, spoiled or tainted food, and obesity. Liver is the most important body organ as it performs the detoxification process. As a result, early disease detection plays a crucial role in the disease diagnosis and recovery process. Early prediction of liver disease has been made possible with the introduction of machine learning technology. This technology provides significant benefits to the healthcare sector by developing new ways to deploy early disease prediction system even in a remote location. SVM, KNN, K-Mean clustering, neural networks, decision trees, and other machine learning techniques are used to implement liver disease diagnosis in order to provide varying levels of accuracy, precision and sensitivity. Unsupervised learning, supervised learning, semi-supervised learning, and reinforcement learning can also be used. This research study intends to compare all the machine learning algorithms to investigate and predict the liver disease and the resultant performance is evaluated based on sensitivity, relevance, accuracy, and precision.