A Comparative Analysis of Liver Cancer Detection using Neural Network Algorithm and Correlation Algorithm

The fast development of machine learning and AI in recent years has made it a widely used method. Because of its widespread use and effective pattern recognition and classification techniques, big institutions have begun funding research in the healthcare sector to boost the precision with which illnesses may be predicted. However, there are a number of difficulties in applying these methods. A significant barrier is the lack of a sufficient medical picture data set. This paper aims to provide a modest introduction to deep learning in medical image processing, including both the theoretical foundations and practical implementations of this technique. This thesis explores various major advances in computer science that contribute to the widespread appeal of deep learning (DL). This sheds light on the causes behind deep learning’s meteoric rise in numerous application fields, including medical image processing. The aim was to deploy new methodologies on existing cancer datasets to see if machine learning and deep learning algorithms might be used for the detection of cancer. The purpose of this study was to classify cancer using many text and picture datasets. Liver BUPA disease database is the dataset that is processed using various machine learning and deep learning models. In this paper, we provide optimization algorithms that outperform the existing strategies in terms of efficiency.