Hybrid Optimization and Explainable Deep Learning for Breast Cancer Detection
Breast cancer continues to be one of the leading causes of women’s deaths around the world, and this has emphasized the necessity to have novel and interpretable diagnostic models. This work offers a clear learning deep learning model that integrates the mobility of MobileNet and two bio-driven opti...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/15/8448 |
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| author | Maral A. Mustafa Osman Ayhan Erdem Esra Söğüt |
| author_facet | Maral A. Mustafa Osman Ayhan Erdem Esra Söğüt |
| author_sort | Maral A. Mustafa |
| collection | DOAJ |
| description | Breast cancer continues to be one of the leading causes of women’s deaths around the world, and this has emphasized the necessity to have novel and interpretable diagnostic models. This work offers a clear learning deep learning model that integrates the mobility of MobileNet and two bio-driven optimization operators, the Firefly Algorithm (FLA) and Dingo Optimization Algorithm (DOA), in an effort to boost classification appreciation and the convergence of the model. The suggested model demonstrated excellent findings as the DOA-optimized MobileNet acquired the highest performance of 98.96 percent accuracy on the fusion test, and the FLA-optimized MobileNet scaled up to 98.06 percent and 95.44 percent accuracies on mammographic and ultrasound tests, respectively. Further to good quantitative results, Grad-CAM visualizations indeed showed clinically consistent localization of the lesions, which strengthened the interpretability and model diagnostic reliability of Grad-CAM. These results show that lightweight, compact CNNs can be used to do high-performance, multimodal breast cancer diagnosis. |
| format | Article |
| id | doaj-art-8d6eb1b1b0324a7380e708f22238e2f2 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-8d6eb1b1b0324a7380e708f22238e2f22025-08-20T03:36:35ZengMDPI AGApplied Sciences2076-34172025-07-011515844810.3390/app15158448Hybrid Optimization and Explainable Deep Learning for Breast Cancer DetectionMaral A. Mustafa0Osman Ayhan Erdem1Esra Söğüt2Graduate School of Natural and Applied Sciences, Department of Computer Engineering, Gazi University, Ankara 06560, TürkiyeDepartment of Computer Engineering, Faculty of Technology, Gazi University, Ankara 06560, TürkiyeDepartment of Computer Engineering, Faculty of Technology, Gazi University, Ankara 06560, TürkiyeBreast cancer continues to be one of the leading causes of women’s deaths around the world, and this has emphasized the necessity to have novel and interpretable diagnostic models. This work offers a clear learning deep learning model that integrates the mobility of MobileNet and two bio-driven optimization operators, the Firefly Algorithm (FLA) and Dingo Optimization Algorithm (DOA), in an effort to boost classification appreciation and the convergence of the model. The suggested model demonstrated excellent findings as the DOA-optimized MobileNet acquired the highest performance of 98.96 percent accuracy on the fusion test, and the FLA-optimized MobileNet scaled up to 98.06 percent and 95.44 percent accuracies on mammographic and ultrasound tests, respectively. Further to good quantitative results, Grad-CAM visualizations indeed showed clinically consistent localization of the lesions, which strengthened the interpretability and model diagnostic reliability of Grad-CAM. These results show that lightweight, compact CNNs can be used to do high-performance, multimodal breast cancer diagnosis.https://www.mdpi.com/2076-3417/15/15/8448deep learningtransfer learningbreast cancer detectionoptimization algorithmsexplainable AI (XAI)Grad-CAM |
| spellingShingle | Maral A. Mustafa Osman Ayhan Erdem Esra Söğüt Hybrid Optimization and Explainable Deep Learning for Breast Cancer Detection Applied Sciences deep learning transfer learning breast cancer detection optimization algorithms explainable AI (XAI) Grad-CAM |
| title | Hybrid Optimization and Explainable Deep Learning for Breast Cancer Detection |
| title_full | Hybrid Optimization and Explainable Deep Learning for Breast Cancer Detection |
| title_fullStr | Hybrid Optimization and Explainable Deep Learning for Breast Cancer Detection |
| title_full_unstemmed | Hybrid Optimization and Explainable Deep Learning for Breast Cancer Detection |
| title_short | Hybrid Optimization and Explainable Deep Learning for Breast Cancer Detection |
| title_sort | hybrid optimization and explainable deep learning for breast cancer detection |
| topic | deep learning transfer learning breast cancer detection optimization algorithms explainable AI (XAI) Grad-CAM |
| url | https://www.mdpi.com/2076-3417/15/15/8448 |
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