MobNas ensembled model for breast cancer prediction
Abstract Breast cancer poses a real and immense threat to humankind, thus a need to develop a way of diagnosing this devastating disease early, accurately, and in a simpler manner. Thus, while substantial progress has been made in developing machine learning algorithms, deep learning, and transfer l...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-01920-4 |
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| author | Tariq Shahzad Sheikh Muhammad Saqib Tehseen Mazhar Muhammad Iqbal Ahmad Almogren Yazeed Yasin Ghadi Mamoon M. Saeed Habib Hamam |
| author_facet | Tariq Shahzad Sheikh Muhammad Saqib Tehseen Mazhar Muhammad Iqbal Ahmad Almogren Yazeed Yasin Ghadi Mamoon M. Saeed Habib Hamam |
| author_sort | Tariq Shahzad |
| collection | DOAJ |
| description | Abstract Breast cancer poses a real and immense threat to humankind, thus a need to develop a way of diagnosing this devastating disease early, accurately, and in a simpler manner. Thus, while substantial progress has been made in developing machine learning algorithms, deep learning, and transfer learning models, issues with diagnostic accuracy and minimizing diagnostic errors persist. This paper introduces MobNAS, a model that uses MobileNetV2 and NASNetLarge to sort breast cancer images into benign, malignant, or normal classes. The study employs a multi-class classification design and uses a publicly available dataset comprising 1,578 ultrasound images, including 891 benign, 421 malignant, and 266 normal cases. By deploying MobileNetV2, it is easy to work well on devices with less computational capability than is used by NASNetLarge, which enhances its applicability and effectiveness in other tasks. The performance of the proposed MobNAS model was tested on the breast cancer image dataset, and the accuracy level achieved was 97%, the Mean Absolute Error (MAE) was 0.05, and the Matthews Correlation Coefficient (MCC) was 95%. From the findings of this research, it is evident that MobNAS can enhance diagnostic accuracy and reduce existing shortcomings in breast cancer detection. |
| format | Article |
| id | doaj-art-bdd96a74227a49c8bf059b16a5c0d04d |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-bdd96a74227a49c8bf059b16a5c0d04d2025-08-20T03:22:12ZengNature PortfolioScientific Reports2045-23222025-05-0115111710.1038/s41598-025-01920-4MobNas ensembled model for breast cancer predictionTariq Shahzad0Sheikh Muhammad Saqib1Tehseen Mazhar2Muhammad Iqbal3Ahmad Almogren4Yazeed Yasin Ghadi5Mamoon M. Saeed6Habib Hamam7Department of Computer Engineering, COMSATS University Islamabad, Sahiwal CampusDepartment of Computing and Information Technology, Gomal UniversitySchool of Computer Science, National College of Business Administration and EconomicsDepartment of Computing and Information Technology, Gomal UniversityDepartment of Computer Science, College of Computer and Information Sciences, King Saud UniversityDepartment of Computer Science and Software Engineering, Al Ain UniversityDepartment of Communications and Electronics Engineering, Faculty of Engineering, University of Modern Sciences (UMS)Faculty of Engineering, Uni de MonctonAbstract Breast cancer poses a real and immense threat to humankind, thus a need to develop a way of diagnosing this devastating disease early, accurately, and in a simpler manner. Thus, while substantial progress has been made in developing machine learning algorithms, deep learning, and transfer learning models, issues with diagnostic accuracy and minimizing diagnostic errors persist. This paper introduces MobNAS, a model that uses MobileNetV2 and NASNetLarge to sort breast cancer images into benign, malignant, or normal classes. The study employs a multi-class classification design and uses a publicly available dataset comprising 1,578 ultrasound images, including 891 benign, 421 malignant, and 266 normal cases. By deploying MobileNetV2, it is easy to work well on devices with less computational capability than is used by NASNetLarge, which enhances its applicability and effectiveness in other tasks. The performance of the proposed MobNAS model was tested on the breast cancer image dataset, and the accuracy level achieved was 97%, the Mean Absolute Error (MAE) was 0.05, and the Matthews Correlation Coefficient (MCC) was 95%. From the findings of this research, it is evident that MobNAS can enhance diagnostic accuracy and reduce existing shortcomings in breast cancer detection.https://doi.org/10.1038/s41598-025-01920-4MobileNetV2NASNetLargeMachine learningDeep learningTransfer learningMean absolute error |
| spellingShingle | Tariq Shahzad Sheikh Muhammad Saqib Tehseen Mazhar Muhammad Iqbal Ahmad Almogren Yazeed Yasin Ghadi Mamoon M. Saeed Habib Hamam MobNas ensembled model for breast cancer prediction Scientific Reports MobileNetV2 NASNetLarge Machine learning Deep learning Transfer learning Mean absolute error |
| title | MobNas ensembled model for breast cancer prediction |
| title_full | MobNas ensembled model for breast cancer prediction |
| title_fullStr | MobNas ensembled model for breast cancer prediction |
| title_full_unstemmed | MobNas ensembled model for breast cancer prediction |
| title_short | MobNas ensembled model for breast cancer prediction |
| title_sort | mobnas ensembled model for breast cancer prediction |
| topic | MobileNetV2 NASNetLarge Machine learning Deep learning Transfer learning Mean absolute error |
| url | https://doi.org/10.1038/s41598-025-01920-4 |
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