Liver Cancer Data Feature Extraction Using Pre-Trained Neural Network With Principal Component Analysis

Abstract

Liver cancer is a critical health issue with a high mortality rate, necessitating accurate and timely diagnosis. In this paper,we propose an approach for liver cancer data feature extraction, combining pre-trained neural networks and principal component analysis (PCA). By leveraging the knowledge learned from a pre-trained neural network, research extract relevant features from liver cancer data. Subsequently, PCA is applied to reduce the dimensionality of the extracted features, while retaining their discriminatory power. Experimental results demonstrate that proposed approach significantly enhances the accuracy of liver cancer diagnosis compared to traditional methods. The integration of pre-trained neural networks with PCA holds promise for improving the effectiveness and efficiency of liver cancer detection and prognosis.