eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2025-06-30 18 2 1334 1343 10.13005/bpj/3172 65558 article Premalatha Ravi 1 Jayanthi Krishnasamy Balasundaram 1 Rajasekaran Chinnappan 1 Sureshkumar Ramasamy 2 Department of ECE, K.S.Rangasamy College of Technology, Namakkal, India Radiation Oncologist, Erode Cancer Centre, Erode, India. (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. https://biomedpharmajournal.org/vol18no2/comparative-analysis-of-transfer-learning-models-for-breast-cancer-detection-leveraging-pre-trained-networks-for-enhanced-diagnostic-accuracy/ Breast Cancer CNN Histopathological Images Pre-trained Models Transfer Learning