Developing liver cancer drug response prediction system using late fusion of reduced deep features

Abstract

Liver cancer is a major cause of death, with a high mortality rate worldwide. For better treatment and rehabilitation of liver cancer, assessing and evaluating drug delivery and response prediction is vital. In this study, we propose a novel approach for the prediction of the liver anticancer drug response using modified ResNet101 deep learning with transfer learning (TL) concept for deep features extraction. Dimension reduction algorithms, PCA and t-SNE are applied to capture global and local structures of deep features, respectively. Herein, a new fusion scheme is introduced that uses global and local levels of image information to improve the classification. Reduced deep fused features are used to develop a quadratic discriminant analysis (QDA) prediction model. The proposed approach is evaluated on fluorescent images of human hepatocellular carcinoma (HepG2) treated using an anticancer drug functionalized by cobalt ferrite@barium titanate (CFO@BTO) nanoparticles. A dataset comprising 203 HepG2 microscopic images is used to train and test the model using the split ratio of 75%:25% by employing a 5-fold cross-validation technique. The proposed system achieved a high accuracy of 98.0% compared to other state-of-the-art approaches. The developed pipeline is flexible and can be extended for prostate, lung, and breast cancers.