Experiments on Gaussian Dropout and Adadelta for Hepatocellular Carcinoma

Abstract

Abstract: Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer. Hepatocellular carcinoma occurs most often in people with chronic liver diseases, such as cirrhosis caused by hepatitis B or hepatitis C infection. Early detection and accurate predictive analysis play a pivotal role in the totality of the human population and are of extreme importance for enhanced life expectancy. With the advent of computation, there are well-defined publicly available datasets that can be leveraged for an accurate and temporarily efficient understanding of HCC. There exists preliminary work on these data samples that leverage classical machine learning algorithms, however, the state of the art is heavily skewed towards the deep neural networks. To improve the existing approaches, this paper seeks to leverage Gaussian Dropout, a variant of the standard dropout, for its remedial action on overfitting and related qualities. The pipeline is also tested and experimented with Adadelta, to obtain the applicability of these additions to a standard feed-forward network. These experiments and the methodologies considered for appendage to the baseline network are thoroughly assessed and validated by using the accepted metrics on an iteratively imputed dataset on multiple train-test data distributions.