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@article{163188,
        author = {BABU G and ARUN M and ASWIN KUMAR S and BHADRIPRASATH B J and DHARSHINIPRIYA V},
        title = {LIVER TUMOR DETECTION USING DEEP LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {11},
        pages = {844-847},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=163188},
        abstract = {Liver tumor poses a significant global health challenge, requiring accurate and cost-effective diagnostic solutions. In this study, we propose leveraging Deep Learning algorithms to streamline Liver Tumor diagnosis, aiming for improved efficiency and reduced costs. Traditional liver tumor diagnostic methods are complex and expensive, hindering accessibility to healthcare. This study addresses the need for more efficient and accurate diagnostic approaches, particularly in identifying early-stage tumor cases. We present a novel approach using Deep Learning algorithms for automated liver tumor diagnosis. By the use of these technology, our model aims to provide timely and cost-effective solutions, revolutionizing liver tumor diagnosis. As we compare our solution with the existing system, we make use of MRI scan instead of CT scan and provides more accuracy and reduces false alarms. By using these technologies, we can reduce the human biasness in detecting the tumor in a cost- efficient manner.},
        keywords = {Liver tumor, Deep Learning, Cost-effectiveness, MRI scan.},
        month = {},
        }