A transfer learning approach for the classification of liver cancer

• Published in: Journal of intelligent systems Vol. 32; no. 1; pp. 1 - 14
• Main Authors: Abdulsahib, Fatimah I., Al-Khateeb, Belal, Kóczy, László T., Nagy, Szilvia
• Format: Journal Article
• Language: English
• Published: Berlin De Gruyter 31.12.2023; Walter de Gruyter GmbH
• Subjects: Accuracy; Artificial neural networks; Classification; Computed tomography; Datasets; Deep learning; Liver cancer; liver cancer classification; liver CT; Machine learning; Medical electronics; Medical imaging; MobileNetV2; Neural networks; Radiology; ResNet-50; Sensitivity; Tomography; transfer learning; Tumors; VGG-16; Iraq
• ISSN: 2191-026X; 0334-1860; 2191-026X
• DOI: 10.1515/jisys-2023-0119

Abstract Problem 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. Aim This article classifies liver tumors and identifies whether they are malignant, benign tumor, or normal liver. Methods This study mainly focuses on computed tomography scans from the Radiology Institute in Baghdad Medical City, Iraq, and provides a novel transfer learning (TL) approach for the categorization of liver cancer using medical images. Our findings show that the TL-based model performs better at classifying data, as in our method, high-level characteristics from liver images are extracted using pre-trained convolutional neural networks compared to conventional techniques and DL models that do not use TL. Results The proposed method using models of TL technology (VGG-16, ResNet-50, and MobileNetV2) successfully achieves high accuracy, sensitivity, and specificity in identifying liver cancer, making it an important tool for radiologists and other healthcare professionals. The experiment results show that the diagnostic accuracy in the VGG-16 model is up to 99%, ResNet-50 model 100%, and 99% total classification accuracy was attained with the MobileNetV2 model. Conclusion This proves the improvement of models when working on a small dataset. The use of new layers also showed an improvement in the performance of the classifiers, which accelerated the process.