Breast cancer is a major cause of concern on a global scale due to its high incidence rate. It is one of the leading causes of death for women, if left untreated. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is increasingly being used in the evaluation of breast cancer. Prior studies neglected to take into account breast cancer characteristics and features that might be helpful for distinguishing the four molecular subtypes of breast cancer. The use of breast DCE-MRI to identify the molecular subtypes is now the focus of research in breast cancer analysis. It offers breast cancer patients a better chance for an early and effective treatment plan. A manually annotated dataset of 1359 DCE-MRI images was used in this study, with 70% used for training and the remaining for testing. Twelve deep features were extracted from this dataset. The dataset was initially preprocessed through placing the ROIs by a radiologist experienced in breast MRI interpretation, then deep features are extracted using the proposed convolutional neural network (CNN). Finally, the deep features extracted are classified into molecular subtypes of breast cancer using the support vector machine (SVM). The effectiveness of the predictive model was assessed using accuracy and area under curve (AUC) measures. The test was performed on unseen held-out data. The maximum achieved accuracy and AUC were 99.78% and 100% respectively, with substantially a low complexity rate.
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2024/2/1
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