Transfer Learning Approach For Multi-Modal Liver Cancer Classification
Keywords:
Classification, Liver cancer, Liver lesion, Multi-modal, Transfer learningAbstract
Liver cancer is a leading cause of cancer-related deaths worldwide. This study proposes a multi-modal transfer learning approach to classify liver conditions from CT,a MRI, and ultrasound images into normal, benign, and malignant categories. Pre-trained models (VGG16, VGG19, ResNet50, InceptionV3) were fine-tuned using a dataset of 4,050 images from Tikur Anbessa Referral Hospital, preprocessed via resizing and rescaling. Performance was evaluated using accuracy, precision, recall, and F1-score. VGG19 achieved the best results with 90% accuracy and 97% malignant tumor detection, demonstrating its superiority over other models and its suitability for clinical decision-making. The results highlight the advantages of multi-modal imaging over single-modality approaches in reducing diagnostic errors. This transfer learning model effectively leverages pre-trained features to overcome data limitations and aids radiologists by providing rapid, consistent analysis for improved patient outcomes.
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