article · Scientific Reports
Breast cancer represents the highest cancer incidence among women in Ethiopia, where late-stage detection frequently delays or prevents effective cures. While mammography offers an established method for early detection, interpreting images requires experienced breast radiologists, a limited resource in the country. To assist healthcare practitioners with screening and patient prioritisation, a model was developed combining an ensemble of EfficientNet-based classifiers with a YOLOv5 suspicious mass detection method. The integration of YOLOv5 provides explanations for classifier predictions and improves sensitivity by catching abnormalities missed by the classifier alone. In testing, the classifier achieved an F1-score of 0.87 and a sensitivity of 0.82. Integrating suspicious mass detection increased sensitivity to 0.89, accompanied by an F1-score of 0.79.
Early detection is vital for successful cancer treatment, yet severe shortages of specialist radiologists lead to late diagnoses. Using deep learning to interpret mammograms and highlight suspicious masses provides clinical support that can help triage patients and ensure fewer life-threatening abnormalities are missed in resource-constrained healthcare environments.
This software can serve as a diagnostic decision-support tool to assist hospital radiology units and screening clinics with patient prioritisation. The abstract demonstrates applied algorithm development and performance testing, indicating that the technology is at an applied research stage that still requires clinical workflow integration and validation before practical deployment.
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Breast cancer has the highest incidence rate among women in Ethiopia compared to other types of cancer. Unfortunately, many cases are detected at a stage where a cure is delayed or not possible. To address this issue, mammography-based screening is widely accepted as an effective technique for early detection. However, the interpretation of mammography images requires experienced radiologists in breast imaging, a resource that is limited in Ethiopia. In this research, we have developed a model to assist radiologists in mass screening for breast abnormalities and prioritizing patients. Our approach combines an ensemble of EfficientNet-based classifiers with YOLOv5, a suspicious mass detection method, to identify abnormalities. The inclusion of YOLOv5 detection is crucial in providing explanations for classifier predictions and improving sensitivity, particularly when the classifier fails to detect abnormalities. To further enhance the screening process, we have also incorporated an abnormality detection model. The classifier model achieves an F1-score of 0.87 and a sensitivity of 0.82. With the addition of suspicious mass detection, sensitivity increases to 0.89, albeit at the expense of a slightly lower F1-score of 0.79.
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DOI: 10.1038/s41598-023-50797-8
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