article · PLoS ONE
Manual analysis of breast histopathology images is often time-consuming and susceptible to diagnostic errors. To address these challenges, a hybrid deep learning model combining convolutional neural networks with EfficientNetV2B3 was developed to detect invasive ductal carcinoma. The system analyses whole slide images to differentiate between positive invasive ductal carcinoma tissue and negative tissue, assisting pathologists in establishing accurate diagnoses. In comparative evaluations, the hybrid model achieved an accuracy of 96.3 percent, a precision of 93.4 percent, a recall of 86.4 percent, and an area under the receiver operating characteristic curve of 97.5 percent. The architecture was tested against alternative model pairings, including MobileNet with DenseNet121 and MobileNetV2 with EfficientNetV2B0, and proved more effective than these and other contemporary machine learning approaches.
Breast cancer remains a widespread and life-threatening condition worldwide, making prompt and reliable diagnosis essential for effective treatment. By automating the identification of invasive ductal carcinoma in digital tissue slides, this computational approach can assist pathologists in reducing diagnostic errors and cutting down the substantial time required for manual microscopic evaluation.
The model provides an algorithmic foundation for digital pathology software, designed to assist pathologists during whole slide image interpretation. As the abstract only presents computational testing against alternative deep learning baselines on image datasets, the technology represents early-stage software research that requires validation and integration into clinical diagnostic workflows before real-world deployment can occur.
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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'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.
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DOI: 10.1371/journal.pone.0296912
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