A Paradigm Shift in Breast Cancer Detection: Leveraging Transfer Learning for Enhanced Diagnostic Accuracy

Breast cancer continues to be a major global health issue, emphasizing the importance of precise and prompt detection to enhance the well-being of patients. In this study, a novel approach is proposed for breast cancer detection using transfer learning with the InceptionV3 model. Our research focuses on analyzing histopathology images to classify breast tissue samples as benign or malignant. Additionally, the effectiveness of alternative models, including ResNet, DenseNet, NasNet, and NasNet Mobile, for the same classification task is explored. To train the models, a comprehensive dataset comprising a diverse range of histopathology images is utilizsed. Transfer learning enabled us to leverage the rich representations learned by InceptionV3, ResNet, DenseNet, NasNet, and NasNet Mobile on large-scale image datasets, which greatly expedited the training process and enhanced overall classification performance. Through extensive evaluations, the superior performance of the InceptionV3 model in accurately distinguishing between benign and malignant breast tissue samples is highlighted. The outcomes of this research present a significant step forward in breast cancer detection, showcasing the potential of transfer learning and the utility of InceptionV3 as a robust classifier for histopathology images. The findings also shed light on the comparative performance of alternative deep learning models, providing valuable insights for future research and clinical applications. Ultimately, this study contributes to the ongoing efforts in improving early detection and diagnosis of breast cancer, paving the way for enhanced patient care and outcomes.