Review on Liver Cirrhosis Detection using Machine Learning and Deep Learning Techniques

Abstract

Liver illnesses account for further than 2.8% of all fatalities in India each year. However, it can be challenging to spot liver disease in the early stages when the symptoms are minor. The majority of the time, symptoms of liver illness don't appear unless a critical phase has been reached, making it difficult to recognize and diagnose. Therefore, a thorough literature survey is conducted that aims in identifying liver diseases among patients by employing different techniques. The paper starts by discussing briefly about liver and various diseases related to it. Moreover, we have also analysed the impact of covid-19 on liver. After analysing the literature survey, it has been analysed that majority of researchers are working majorly with two classes of Machine Learning (ML) and Deep Learning (DL). However, due to some limitations in these models, authors started to shift their attention towards Nature Inspired Optimization Algorithms. We have reviewed some of the latest works in these three categories and at the end of each category a comparison table and inferences are given. Moreover, it has also been observed that by using optimization algorithms along with ML algorithms, the accuracy of liver disease detection models is enhanced significantly.