Comparative Analysis of Transfer Learning Models for Breast Cancer Detection: Leveraging Pre-Trained Networks for Enhanced Diagnostic Accuracy

(Breast cancer represents the most prevalent variant of malignancy observed in the female population) 2.3 million were diagnosed with breast cancer in 2022. Early detection enhances the quality of life for breast cancer patients, and one promising approach to achieving this is through the analysis of histopathological images using pre-trained models of convolutional neural networks (CNNs) architectures, namely ResNet152, InceptionV3, and MobileNetV2, all initially trained on the ImageNet dataset. This paper presents an analysis of these architectures applied to the breast cancer dataset, comparing their robustness and effectiveness in detecting breast cancer. The results demonstrate that models pre-trained on ImageNet perform significantly better compared to the same architectures trained from scratch on the breast cancer dataset. This difference in performance highlights the importance of transfer learning in analyzing medical images. It shows that using models already trained on large and varied datasets like ImageNet can greatly improve the ability to identify features in histopathological images. The results help to decide the robustness of the architectures for the given dataset. The results will support researchers working in this domain to understand which architecture yields better results in breast cancer diagnosis.