01860nas a2200217   4500000000100000000000100001000000100002008004100003260003400044100001600078700002100094700002000115700002100135700001800156700002000174245006400194856004900258300000900307490000700316520131900323       2024                            d  c01bPublic Library of Science1 aSameh Zarif1 aHatem Abdulkader1 aIbrahim Elaraby1 aAbdullah Alharbi1 aWail Elkilani1 aPaweł Pławiak00aUsing hybrid pre-trained models for breast cancer detection  uhttps://doi.org/10.1371/journal.pone.0296912  a1-210 v193 aBreast cancer is a prevalent and life-threatening disease that affects women globally. Early detection and access to top-notch treatment are crucial in preventing fatalities from this condition. However, manual breast histopathology image analysis is time-consuming and prone to errors. This study proposed a hybrid deep learning model (CNN+EfficientNetV2B3). The proposed approach utilizes convolutional neural networks (CNNs) for the identification of positive invasive ductal carcinoma (IDC) and negative (non-IDC) tissue using whole slide images (WSIs), which use pre-trained models to classify breast cancer in images, supporting pathologists in making more accurate diagnoses. The proposed model demonstrates outstanding performance with an accuracy of 96.3%, precision of 93.4%, recall of 86.4%, F1-score of 89.7%, Matthew’s correlation coefficient (MCC) of 87.6%, the Area Under the Curve (AUC) of a Receiver Operating Characteristic (ROC) curve of 97.5%, and the Area Under the Curve of the Precision-Recall Curve (AUPRC) of 96.8%, which outperforms the accuracy achieved by other models. The proposed model was also tested against MobileNet+DenseNet121, MobileNetV2+EfficientNetV2B0, and other deep learning models, proving more powerful than contemporary machine learning and deep learning approaches.