@article{bibcite_16010,
  author = {Sameh Zarif and Hatem Abdulkader and Pawel Plawiak and Abdullah Alharbi},
  title = {Using hybrid pre-trained models for breast cancer detection},
  abstract = {<p>Breast 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{\textquoteright}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.</p>
},
  year = {2024},
  journal = {Public Library of Science},
  url = {https://doi.org/10.1371/journal.pone.0296912},
  doi = {10.1371/journal.pone.0296912},
  language = {eng},
}