Abstract
Invasive ductal carcinoma (IDC) is a common form of breast cancer that affects women. In traditional medical practice, physicians have to manually test and classify areas which are suspected to be cancerous. However, the literature strongly indicates that the manual segmentation process performed by medical practitioners is neither time efficient nor accurate, as it relies on their subjective judgment. This paper introduces a model called residual attention neural network breast cancer classification (RANN-BCC) to help medical practitioners in the cancer diagnostic process. RANN-BCC utilizes residual neural network (ResNet) as an expert-supportive method to aid medical practitioners in cancer diagnosis. The implementation of RANN-BCC can support the classification of whole slide imaging (WSI) into non-IDC and IDC without prior information about the presence of a cancerous lesion. The classification results demonstrate that the RANN-BCC model has achieved 92.45% accuracy, 0.98 recall, 0.91 precision, and 0.94 F-score which has outperformed other models such as CNN, AlexNet, Residual Neural Network 34 (ResNet34), and Feed-Forward Neural Network. The developed RANN-BCC model aims to help medical experts to classify IDC and non-IDC of breast cancer by learning the feature content of medical images.
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The data can be obtained from the following link: https://www.kaggle.com/paultimothymooney/breast-histopathology-images.
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The source code of the current work is available from the corresponding author on reasonable request.
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Toa, C.K., Elsayed, M. & Sim, K.S. Deep residual learning with attention mechanism for breast cancer classification. Soft Comput 28, 9025–9035 (2024). https://doi.org/10.1007/s00500-023-09152-2
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DOI: https://doi.org/10.1007/s00500-023-09152-2