Authors : E Pavan Kumar; Habibur Rahaman; Chityala Vishnuvardhan Reddy; Rokkam Sahil; Dr. Shwetha Buchanalli; Bharani Kumar Depuru

Volume/Issue : Volume 9 - 2024, Issue 6 - June

Google Scholar : https://tinyurl.com/ycshs7t3

Scribd : https://tinyurl.com/26wxjwse

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUN1368

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This research investigates the application of deep learning techniques to enhance the diagnostic accuracy of liver tumour classification in collaboration with a prominent hospital in South India. By leveraging a carefully curated dataset of histopathological images, we evaluated the performance of several advanced deep learning architectures, including DenseNet 121, ResNet50, and VGG16. Our findings reveal that DenseNet121 outperformed the other models, achieving the highest accuracy in both training and testing phases, thus exceeding our predefined accuracy benchmarks. The superior performance of DenseNet121 is attributed to its dense connectivity, which facilitates improved feature and gradient propagation throughout the network. This study highlights the significant potential of AI-driven diagnostics in enhancing liver tumour classification, thereby optimizing the diagnostic workflow and providing substantial benefits for patient care and healthcare system efficiency.

Keywords : Deep Learning Models, Classification, Cholangiocarcinoma (CC), Hepatocellular Carcinoma (HCC).

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