A transfer learning approach for the classification of liver cancer

Abstract

The frequency of liver cancer is rising worldwide, and it is a common, deadly condition. For successful treatment and patient survival, early and precise diagnosis is essential. The automated classification of liver cancer using medical imaging data has shown potential outcome when employing machine and deep learning (DL) approaches. To train deep neural networks, it is still quite difficult to obtain a large and diverse dataset, especially in the medical field.

Keywords

• liver cancer classification
• deep learning
• transfer learning
• liver ct
• vgg-16
• resnet-50
• mobilenetv2