Topics in magnetic resonance imaging : TMRI · 2026
Automated Diagnosis of Breast Cancer Using Deep Learning Techniques Applied to Digital Mammography and Magnetic Resonance Images.
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Alakhras Maram, Tayseer Inas, Alquran Hiam, Alsalatie Mohammed, Melhem Dua'a Bani
Аннотация
UNLABELLED: Introduction:Breast cancer is a global issue impacting women's well-being, highlighting the importance of early detection to improve treatment outcomes and decrease mortality rates. This study aimed to assess various AI methodologies to classify breast images into normal, benign, and malignant.
METHODS: A hybrid of 4 CNN-pertained networks-Res-Net18, Mobile-Net, Shuffle-Net, and Inception-V3-were applied on 269 mammograms and 267 dynamic contrast-enhanced MRI examinations. Transfer learning was used, adapting the last fully connected layers to classify 3 classes (normal, benign, and malignant) resulting in 12 features. Support vector machine was employed to categorize images. Classifier performances were evaluated using the confusion matrix, accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating curve (AUC-ROC).
RESULTS: Res-Net model achieved the highest accuracy, sensitivity, and specificity of 90.89%, 90.93%, and 95.39%, respectively. Whereas Shuffle-Net displayed the lowest accuracy of 84.76%. The AUC ranged between 0.95 and 0.97 among pretrained networks while it was higher (0.99) for the hybrid model. For MRI image classification, the Mobile-net network recorded the highest accuracy, sensitivity, and specificity of 88.55%, 88.49%, and 94.22%, respectively, while the Res-Net exhibited the lowest accuracy of 84.35%. The AUC ranged between 0.94 and 0.96 among pretrained networks while it was higher (0.98) for the Hybrid model.
CONCLUSION: ResNet-18 showed the most optimal model for extracting features from mammograms compared with other CNN networks (Mobile-Net, Shuffle-Net, and Inception-V3) while Mobile-Net model was the most suitable in MRI. The effectiveness of deep learning in accurately classifying mammograms and MRI images can be improved by using a hybrid model.
METHODS: A hybrid of 4 CNN-pertained networks-Res-Net18, Mobile-Net, Shuffle-Net, and Inception-V3-were applied on 269 mammograms and 267 dynamic contrast-enhanced MRI examinations. Transfer learning was used, adapting the last fully connected layers to classify 3 classes (normal, benign, and malignant) resulting in 12 features. Support vector machine was employed to categorize images. Classifier performances were evaluated using the confusion matrix, accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating curve (AUC-ROC).
RESULTS: Res-Net model achieved the highest accuracy, sensitivity, and specificity of 90.89%, 90.93%, and 95.39%, respectively. Whereas Shuffle-Net displayed the lowest accuracy of 84.76%. The AUC ranged between 0.95 and 0.97 among pretrained networks while it was higher (0.99) for the hybrid model. For MRI image classification, the Mobile-net network recorded the highest accuracy, sensitivity, and specificity of 88.55%, 88.49%, and 94.22%, respectively, while the Res-Net exhibited the lowest accuracy of 84.35%. The AUC ranged between 0.94 and 0.96 among pretrained networks while it was higher (0.98) for the Hybrid model.
CONCLUSION: ResNet-18 showed the most optimal model for extracting features from mammograms compared with other CNN networks (Mobile-Net, Shuffle-Net, and Inception-V3) while Mobile-Net model was the most suitable in MRI. The effectiveness of deep learning in accurately classifying mammograms and MRI images can be improved by using a hybrid model.