A quantum-optimized approach for breast cancer detection using SqueezeNet-SVM
Abstract Breast cancer is one of the most aggressive types of cancer, and its early diagnosis is crucial for reducing mortality rates and ensuring timely treatment. Computer-aided diagnosis systems provide automated mammography image processing, interpretation, and grading. However, since the curren...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-025-86671-y |
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| author | Anas Bilal Ali Alkhathlan Faris A. Kateb Alishba Tahir Muhammad Shafiq Haixia Long |
| author_facet | Anas Bilal Ali Alkhathlan Faris A. Kateb Alishba Tahir Muhammad Shafiq Haixia Long |
| author_sort | Anas Bilal |
| collection | DOAJ |
| description | Abstract Breast cancer is one of the most aggressive types of cancer, and its early diagnosis is crucial for reducing mortality rates and ensuring timely treatment. Computer-aided diagnosis systems provide automated mammography image processing, interpretation, and grading. However, since the currently existing methods suffer from such issues as overfitting, lack of adaptability, and dependence on massive annotated datasets, the present work introduces a hybrid approach to enhance breast cancer classification accuracy. The proposed Q-BGWO-SQSVM approach utilizes an improved quantum-inspired binary Grey Wolf Optimizer and combines it with SqueezeNet and Support Vector Machines to exhibit sophisticated performance. SqueezeNet’s fire modules and complex bypass mechanisms extract distinct features from mammography images. Then, these features are optimized by the Q-BGWO for determining the best SVM parameters. Since the current CAD system is more reliable, accurate, and sensitive, its application is advantageous for healthcare. The proposed Q-BGWO-SQSVM was evaluated using diverse databases: MIAS, INbreast, DDSM, and CBIS-DDSM, analyzing its performance regarding accuracy, sensitivity, specificity, precision, F1 score, and MCC. Notably, on the CBIS-DDSM dataset, the Q-BGWO-SQSVM achieved remarkable results at 99% accuracy, 98% sensitivity, and 100% specificity in 15-fold cross-validation. Finally, it can be observed that the performance of the designed Q-BGWO-SQSVM model is excellent, and its potential realization in other datasets and imaging conditions is promising. The novel Q-BGWO-SQSVM model outperforms the state-of-the-art classification methods and offers accurate and reliable early breast cancer detection, which is essential for further healthcare development. |
| format | Article |
| id | doaj-art-c54ce8f264384ceb9aad1ab5f4f1ddd9 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c54ce8f264384ceb9aad1ab5f4f1ddd92025-01-26T12:30:40ZengNature PortfolioScientific Reports2045-23222025-01-0115113210.1038/s41598-025-86671-yA quantum-optimized approach for breast cancer detection using SqueezeNet-SVMAnas Bilal0Ali Alkhathlan1Faris A. Kateb2Alishba Tahir3Muhammad Shafiq4Haixia Long5College of Information Science and Technology, Hainan Normal UniversityDepartment of Computer Science, Faculty of Computing and Information Technology, King AbdulAziz UniversityDepartment of Information Technology, Faculty of Computing and Information Technology, King AbdulAziz UniversityShifa College of Medicine, Shifa Tamere Milat UniversitySchool of Information Engineering, Qujing Normal UniversityCollege of Information Science and Technology, Hainan Normal UniversityAbstract Breast cancer is one of the most aggressive types of cancer, and its early diagnosis is crucial for reducing mortality rates and ensuring timely treatment. Computer-aided diagnosis systems provide automated mammography image processing, interpretation, and grading. However, since the currently existing methods suffer from such issues as overfitting, lack of adaptability, and dependence on massive annotated datasets, the present work introduces a hybrid approach to enhance breast cancer classification accuracy. The proposed Q-BGWO-SQSVM approach utilizes an improved quantum-inspired binary Grey Wolf Optimizer and combines it with SqueezeNet and Support Vector Machines to exhibit sophisticated performance. SqueezeNet’s fire modules and complex bypass mechanisms extract distinct features from mammography images. Then, these features are optimized by the Q-BGWO for determining the best SVM parameters. Since the current CAD system is more reliable, accurate, and sensitive, its application is advantageous for healthcare. The proposed Q-BGWO-SQSVM was evaluated using diverse databases: MIAS, INbreast, DDSM, and CBIS-DDSM, analyzing its performance regarding accuracy, sensitivity, specificity, precision, F1 score, and MCC. Notably, on the CBIS-DDSM dataset, the Q-BGWO-SQSVM achieved remarkable results at 99% accuracy, 98% sensitivity, and 100% specificity in 15-fold cross-validation. Finally, it can be observed that the performance of the designed Q-BGWO-SQSVM model is excellent, and its potential realization in other datasets and imaging conditions is promising. The novel Q-BGWO-SQSVM model outperforms the state-of-the-art classification methods and offers accurate and reliable early breast cancer detection, which is essential for further healthcare development.https://doi.org/10.1038/s41598-025-86671-yQuantum computingBreast cancerSupport vector machineGrey wolf optimizationMedical image analysis |
| spellingShingle | Anas Bilal Ali Alkhathlan Faris A. Kateb Alishba Tahir Muhammad Shafiq Haixia Long A quantum-optimized approach for breast cancer detection using SqueezeNet-SVM Scientific Reports Quantum computing Breast cancer Support vector machine Grey wolf optimization Medical image analysis |
| title | A quantum-optimized approach for breast cancer detection using SqueezeNet-SVM |
| title_full | A quantum-optimized approach for breast cancer detection using SqueezeNet-SVM |
| title_fullStr | A quantum-optimized approach for breast cancer detection using SqueezeNet-SVM |
| title_full_unstemmed | A quantum-optimized approach for breast cancer detection using SqueezeNet-SVM |
| title_short | A quantum-optimized approach for breast cancer detection using SqueezeNet-SVM |
| title_sort | quantum optimized approach for breast cancer detection using squeezenet svm |
| topic | Quantum computing Breast cancer Support vector machine Grey wolf optimization Medical image analysis |
| url | https://doi.org/10.1038/s41598-025-86671-y |
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