METHODS: The proposed MPa-DCAE model leverages the hierarchical feature extraction capabilities of VGG19 within a DCAE framework, designed to capture intricate patterns in histopathology images. By using a multi-patch approach, regions of interest are extracted from pathology images to facilitate localized feature learning, enhancing the model's discriminatory power. The auto-encoder component enables unsupervised feature learning, increasing resilience and adaptability to variations in image features. Experiments were conducted at various magnifications on the CBIS-DDSM and MIAS datasets to validate model performance.
RESULTS: Experimental results demonstrated that the MPa-DCAE model outperformed existing methods. For the CBIS-DDSM dataset, the model achieved a precision of 97.96%, a recall of 94.85%, and an accuracy of 98.36%. For the MIAS dataset, it achieved a precision of 97.99%, a recall of 97.2%, and an accuracy of 98.95%. These results highlight the model's robustness and potential for clinical application in computer-assisted diagnosis.
CONCLUSION: The MPa-DCAE model, integrating VGG19 and DCAE, proves to be an effective, automated approach for diagnosing breast cancer. Its high accuracy and generalizability make it a promising tool for clinical practice, potentially improving patient care in histopathology-based breast cancer diagnosis.