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Liver cancer remains a significant global health concern due to its high mortality rates and the scarcity of reliable methods for early-stage detection. In response to this challenge, this study introduces an advanced deep learning framework based on the Residual Neural Network (ResNet) architecture to enhance the prediction of liver cancer. Leveraging the strengths of ResNet in managing complex data structures and extracting critical patterns, the model is trained and tested on a comprehensive dataset that includes clinical details, demographic information, imaging results, and biomarker profiles. Model performance is assessed through key metrics such as accuracy, sensitivity, specificity, and the area under the ROC curve (AUC-ROC). Additionally, feature importance analysis is performed to determine which input variables most significantly influence prediction outcomes. Experimental results reveal that the ResNet-based system achieves high predictive performance, surpassing conventional machine learning approaches. This work contributes to the growing field of AI-driven medical diagnostics and supports efforts aimed at facilitating earlier detection and improved clinical management of liver cancer.
Keywords:
Keywords: Liver Cancer Detection, Deep Neural Networks, ResNet Architecture, Clinical Data Analytics, Imaging-Based Diagnosis, Biomarker Evaluation, Predictive Modeling, AUC-ROC, Feature Analysis, AI in Oncology.
Cite Article:
"AI BASED LIVER DISEASE PREDICTION USING CNN ALGORITHM", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.b658-b663, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505175.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator