Advanced AI for Liver Cancer Detection: Vision Transformers, XAI and Contrastive Learning

Liver cancer detection has always stood as a significant challenge in medical diagnostics, largely due to the complexity of interpreting imaging data and the critical need for accurate yet explainable results. This study explored how recent advances in artificial intelligence, specifically Vision Transformers (ViTs), Contrastive Learning, and Explainable AI (XAI), can be combined to address this challenge more effectively. Unlike conventional models, Vision Transformers are particularly good at capturing intricate patterns in medical images, which makes them well-suited for tasks like cancer classification. To improve the model's ability to generalize across different imaging conditions incorporated contrastive learning techniques, essentially teaching the system to recognize subtle distinctions between similar and dissimilar image features. This approach significantly sharpened its performance. Recognizing the importance of transparency in medical AI also integrated explainable AI tools into the model. This helped generate visual and textual cues that explain the system’s predictions, which is crucial for gaining the trust of clinicians who rely on these tools in high-stakes environments. The model was trained on a comprehensive dataset of liver cancer images, including both CT scans and MRIs, sourced from a well-established medical repository. The results were promising: the system reached a classification accuracy of 92 per cent, outperforming standard convolutional neural networks (CNNs) by 8 per cent. Most notably, it showed strong performance in identifying early-stage liver cancer, with 90 per cent sensitivity and 94 per cent specificity, suggesting that it may hold real potential for clinical application.

@article{Anil2025,
  title     = {Advanced AI for Liver Cancer Detection: Vision Transformers, XAI and Contrastive Learning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {7},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {B C Anil and Jayasimha S R and Samitha Khaiyum and T L Divya and Rakshitha Kiran P and Vishal C},
  doi       = {10.14569/IJACSA.2025.0160716},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160716}
}