A Deep Learning-Driven CAD for Breast Cancer Detection via Thermograms: A Compact Multi-Architecture Feature Strategy
Breast cancer continues to be the most common malignancy among women worldwide, presenting a considerable public health issue. Mammography, though the gold standard for screening, has limitations that catalyzed the advancement of non-invasive, radiation-free alternatives, such as thermal imaging (th...
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MDPI AG
2025-06-01
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| author | Omneya Attallah |
| author_facet | Omneya Attallah |
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| description | Breast cancer continues to be the most common malignancy among women worldwide, presenting a considerable public health issue. Mammography, though the gold standard for screening, has limitations that catalyzed the advancement of non-invasive, radiation-free alternatives, such as thermal imaging (thermography). This research introduces a novel computer-aided diagnosis (CAD) framework aimed at improving breast cancer detection via thermal imaging. The suggested framework mitigates the limitations of current CAD systems, which frequently utilize intricate convolutional neural network (CNN) structures and resource-intensive preprocessing, by incorporating streamlined CNN designs, transfer learning strategies, and multi-architecture ensemble methods. Features are primarily obtained from various layers of MobileNet, EfficientNetB0, and ShuffleNet architectures to assess the impact of individual layers on classification performance. Following that, feature transformation methods, such as discrete wavelet transform (DWT) and non-negative matrix factorization (NNMF), are employed to diminish feature dimensionality and enhance computational efficiency. Features from all layers of the three CNNs are subsequently incorporated, and the Minimum Redundancy Maximum Relevance (MRMR) algorithm is utilized to determine the most prominent features. Ultimately, support vector machine (SVM) classifiers are employed for classification purposes. The results indicate that integrating features from various CNNs and layers markedly improves performance, attaining a maximum accuracy of 99.4%. Furthermore, the combination of attributes from all three layers of the CNNs, in conjunction with NNMF, attained a maximum accuracy of 99.9% with merely 350 features. This CAD system demonstrates the efficacy of thermal imaging and multi-layer feature amalgamation to enhance non-invasive breast cancer diagnosis by reducing computational requirements through multi-layer feature integration and dimensionality reduction techniques. |
| format | Article |
| id | doaj-art-47f48ec6fad54202838051fed98434d1 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-47f48ec6fad54202838051fed98434d12025-08-20T03:50:21ZengMDPI AGApplied Sciences2076-34172025-06-011513718110.3390/app15137181A Deep Learning-Driven CAD for Breast Cancer Detection via Thermograms: A Compact Multi-Architecture Feature StrategyOmneya Attallah0Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria 21937, EgyptBreast cancer continues to be the most common malignancy among women worldwide, presenting a considerable public health issue. Mammography, though the gold standard for screening, has limitations that catalyzed the advancement of non-invasive, radiation-free alternatives, such as thermal imaging (thermography). This research introduces a novel computer-aided diagnosis (CAD) framework aimed at improving breast cancer detection via thermal imaging. The suggested framework mitigates the limitations of current CAD systems, which frequently utilize intricate convolutional neural network (CNN) structures and resource-intensive preprocessing, by incorporating streamlined CNN designs, transfer learning strategies, and multi-architecture ensemble methods. Features are primarily obtained from various layers of MobileNet, EfficientNetB0, and ShuffleNet architectures to assess the impact of individual layers on classification performance. Following that, feature transformation methods, such as discrete wavelet transform (DWT) and non-negative matrix factorization (NNMF), are employed to diminish feature dimensionality and enhance computational efficiency. Features from all layers of the three CNNs are subsequently incorporated, and the Minimum Redundancy Maximum Relevance (MRMR) algorithm is utilized to determine the most prominent features. Ultimately, support vector machine (SVM) classifiers are employed for classification purposes. The results indicate that integrating features from various CNNs and layers markedly improves performance, attaining a maximum accuracy of 99.4%. Furthermore, the combination of attributes from all three layers of the CNNs, in conjunction with NNMF, attained a maximum accuracy of 99.9% with merely 350 features. This CAD system demonstrates the efficacy of thermal imaging and multi-layer feature amalgamation to enhance non-invasive breast cancer diagnosis by reducing computational requirements through multi-layer feature integration and dimensionality reduction techniques.https://www.mdpi.com/2076-3417/15/13/7181deep learningfeature fusionthermogram imagingbreast cancer detectionconvolutional neural networksfeature transformation |
| spellingShingle | Omneya Attallah A Deep Learning-Driven CAD for Breast Cancer Detection via Thermograms: A Compact Multi-Architecture Feature Strategy Applied Sciences deep learning feature fusion thermogram imaging breast cancer detection convolutional neural networks feature transformation |
| title | A Deep Learning-Driven CAD for Breast Cancer Detection via Thermograms: A Compact Multi-Architecture Feature Strategy |
| title_full | A Deep Learning-Driven CAD for Breast Cancer Detection via Thermograms: A Compact Multi-Architecture Feature Strategy |
| title_fullStr | A Deep Learning-Driven CAD for Breast Cancer Detection via Thermograms: A Compact Multi-Architecture Feature Strategy |
| title_full_unstemmed | A Deep Learning-Driven CAD for Breast Cancer Detection via Thermograms: A Compact Multi-Architecture Feature Strategy |
| title_short | A Deep Learning-Driven CAD for Breast Cancer Detection via Thermograms: A Compact Multi-Architecture Feature Strategy |
| title_sort | deep learning driven cad for breast cancer detection via thermograms a compact multi architecture feature strategy |
| topic | deep learning feature fusion thermogram imaging breast cancer detection convolutional neural networks feature transformation |
| url | https://www.mdpi.com/2076-3417/15/13/7181 |
| work_keys_str_mv | AT omneyaattallah adeeplearningdrivencadforbreastcancerdetectionviathermogramsacompactmultiarchitecturefeaturestrategy AT omneyaattallah deeplearningdrivencadforbreastcancerdetectionviathermogramsacompactmultiarchitecturefeaturestrategy |