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1
Liver cancer is the fifth most prevalent cancer in men and ninth in women, with over 900,000 new cases reported globally in 2020.
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Conventional liver cancer diagnosis relies on invasive biopsies and manual CT image interpretation, which are time-consuming and require expert radiologists.
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The proposed deep learning framework integrates several state-of-the-art models, achieving 96.97% accuracy in classifying liver tumors from CT scans.
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Explainable AI methods, including SHAP and Grad-CAM, are incorporated to enhance interpretability and build clinical trust in the diagnostic predictions.
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The study emphasizes the need for automated, accurate, and explainable diagnostic tools to address the challenges in liver cancer detection.
