Bridging Traditional and Modern AI Techniques for Breast Cancer Histopathology

One of the biggest healthcare challenges facing the world today is breast cancer, which needs to be effectively controlled with accurate and novel detection techniques. A comparison of breast cancer cases is presented in this paper using histopathology image analysis, focusing on handcrafted features versus transfer learning using pre-trained networks for multi-class classification. Addressing the global shortage of trained pathologists, our research highlights the potential of automated systems to aid in accurate diagnoses. Using digital pathology and computational algorithms, we aim to detect finer details in histopathological images essential for diagnosis. In this work, we assess the effectiveness of pre-trained networks, including VGG16, VGG19, ResNet50, a n d DenseNet.DenseNet121 and ResNet50V2 achieved the highest accuracy of 95.16%, significantly outperforming traditional handcrafted feature methods. This represents a notable improvement, demonstrating the efficacy of transfer learning in enhancing classification accuracy. We emphasize the critical role of AI in enhancing diagnostic accuracy and addressing the global shortage of pathologists, paving the way for advancements in medical diagnostics. We highlight the vital role AI plays in improving diagnostic precision and mitigating the world pathologist shortage, which opens up new avenues for medical diagnostics research and development.