Deep Learning-Based Liver Histopathology Image Classification: State-of-the-Art Techniques and Emerging Trends

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.