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@article{176923,
        author = {K.S.L.Dedeepya and M. Jabez El Samuel and M. Sai Girish and P. Sai Pranavi and M.Pratussha and B. Bala Krishna},
        title = {LIVER TUMOR DETECTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {7632-7637},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176923},
        abstract = {Liver cancer is a serious disease causing many deaths around the world. Finding it early can save lives. This project uses a special type of artificial intelligence called Convolutional Neural Networks (CNNs) to detect liver tumors from medical scans like CT and MRI. By training the system on real medical images, it learns to spot differences between harmless (benign) and dangerous (malignant) tumors. This method shows better results than older machine learning methods and can help doctors diagnose patients faster and more accurately. By leveraging deep learning architectures, the proposed system achieves high precision, recall, and overall classification accuracy in distinguishing between benign and malignant tumors. Comparative analysis with traditional machine learning methods demonstrates the superiority of CNNs in detecting liver tumors with minimal false positives. The results suggest that CNN-based liver tumor detection can significantly aid radiologists in early diagnosis, improving patient outcomes and reducing diagnostic time.},
        keywords = {automation, smart sensing technology, mechanical action, practical solution},
        month = {April},
        }