Efficient hybrid heuristic adopted deep learning framework for diagnosing breast cancer using thermography images
Abstract The most dangerous form of cancer is breast cancer. This disease is life-threatening because of its aggressive nature and high death rates. Therefore, early discovery increases the patient’s survival. Mammography has recently been recommended as diagnosis technique. Mammography, is expensiv...
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-96827-5 |
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| author | Ahmad Y. A. Bani Ahmad Jafar A. Alzubi Manimaran Vasanthan Suresh Babu Kondaveeti J. Shreyas Thella Preethi Priyanka |
| author_facet | Ahmad Y. A. Bani Ahmad Jafar A. Alzubi Manimaran Vasanthan Suresh Babu Kondaveeti J. Shreyas Thella Preethi Priyanka |
| author_sort | Ahmad Y. A. Bani Ahmad |
| collection | DOAJ |
| description | Abstract The most dangerous form of cancer is breast cancer. This disease is life-threatening because of its aggressive nature and high death rates. Therefore, early discovery increases the patient’s survival. Mammography has recently been recommended as diagnosis technique. Mammography, is expensive and exposure the person to radioactivity. Thermography is a less invasive and affordable technique that is becoming increasingly popular. Considering this, a recent deep learning-based breast cancer diagnosis approach is executed by thermography images. Initially, thermography images are chosen from online sources. The collected thermography images are being preprocessed by Contrast Limited Adaptive Histogram Equalization (CLAHE) and contrasting enhancement methods to improve the quality and brightness of the images. Then, the optimal binary thresholding is done to segment the preprocessed images, where optimized the thresholding value using developed Rock Hyraxes Dandelion Algorithm Optimization (RHDAO). A newly implemented deep learning structure StackVRDNet is used for further processing breast cancer diagnosing using thermography images. The segmented images are fed to the StackVRDNet framework, where the Visual Geometry Group (VGG16), Resnet, and DenseNet are employed for constructing this model. The relevant features are extracted usingVGG16, Resnet, and DenseNet, and then obtain stacked weighted feature pool from the extracted features, where the weight optimization is done with the help of RHDAO. The final classification is performed using StackVRDNet, and the diagnosis results are obtained at the final layer of VGG16, Resnet, and DenseNet. A higher scoring method is rated for ensuring final diagnosis results. Here, the parameters present within the VGG16, Resnet, and DenseNet are optimized via the RHDAO to improve the diagnosis results. The simulation outcomes of the developed model achieve 97.05% and 86.86% in terms of accuracy and precision, respectively. The effectiveness of the designed methd is being analyzed via the conventional breast cancer diagnosis models in terms of various performance measures. |
| format | Article |
| id | doaj-art-773568eb3d564605b71bcee10420a87a |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-773568eb3d564605b71bcee10420a87a2025-08-20T02:24:29ZengNature PortfolioScientific Reports2045-23222025-04-0115113010.1038/s41598-025-96827-5Efficient hybrid heuristic adopted deep learning framework for diagnosing breast cancer using thermography imagesAhmad Y. A. Bani Ahmad0Jafar A. Alzubi1Manimaran Vasanthan2Suresh Babu Kondaveeti3J. Shreyas4Thella Preethi Priyanka5Department of Accounting and Finance, Faculty of Business, Middle East UniversityFaculty of Engineering, Al-Balqa Applied UniversityDepartment of Pharmaceutics, SRM College of Pharmacy, Medicine and health sciences, SRM institute of Science and Technology KattankulathurDepartment of Biochemistry, Symbiosis Medical College for Women, Symbiosis International (Deemed University)Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher EducationDepartment of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical SciencesAbstract The most dangerous form of cancer is breast cancer. This disease is life-threatening because of its aggressive nature and high death rates. Therefore, early discovery increases the patient’s survival. Mammography has recently been recommended as diagnosis technique. Mammography, is expensive and exposure the person to radioactivity. Thermography is a less invasive and affordable technique that is becoming increasingly popular. Considering this, a recent deep learning-based breast cancer diagnosis approach is executed by thermography images. Initially, thermography images are chosen from online sources. The collected thermography images are being preprocessed by Contrast Limited Adaptive Histogram Equalization (CLAHE) and contrasting enhancement methods to improve the quality and brightness of the images. Then, the optimal binary thresholding is done to segment the preprocessed images, where optimized the thresholding value using developed Rock Hyraxes Dandelion Algorithm Optimization (RHDAO). A newly implemented deep learning structure StackVRDNet is used for further processing breast cancer diagnosing using thermography images. The segmented images are fed to the StackVRDNet framework, where the Visual Geometry Group (VGG16), Resnet, and DenseNet are employed for constructing this model. The relevant features are extracted usingVGG16, Resnet, and DenseNet, and then obtain stacked weighted feature pool from the extracted features, where the weight optimization is done with the help of RHDAO. The final classification is performed using StackVRDNet, and the diagnosis results are obtained at the final layer of VGG16, Resnet, and DenseNet. A higher scoring method is rated for ensuring final diagnosis results. Here, the parameters present within the VGG16, Resnet, and DenseNet are optimized via the RHDAO to improve the diagnosis results. The simulation outcomes of the developed model achieve 97.05% and 86.86% in terms of accuracy and precision, respectively. The effectiveness of the designed methd is being analyzed via the conventional breast cancer diagnosis models in terms of various performance measures.https://doi.org/10.1038/s41598-025-96827-5Breast cancer classificationThermogram imagesOptimal binary thresholdingWeighted fused featureRock hyraxes dandelion algorithm optimizationStackVDRNet |
| spellingShingle | Ahmad Y. A. Bani Ahmad Jafar A. Alzubi Manimaran Vasanthan Suresh Babu Kondaveeti J. Shreyas Thella Preethi Priyanka Efficient hybrid heuristic adopted deep learning framework for diagnosing breast cancer using thermography images Scientific Reports Breast cancer classification Thermogram images Optimal binary thresholding Weighted fused feature Rock hyraxes dandelion algorithm optimization StackVDRNet |
| title | Efficient hybrid heuristic adopted deep learning framework for diagnosing breast cancer using thermography images |
| title_full | Efficient hybrid heuristic adopted deep learning framework for diagnosing breast cancer using thermography images |
| title_fullStr | Efficient hybrid heuristic adopted deep learning framework for diagnosing breast cancer using thermography images |
| title_full_unstemmed | Efficient hybrid heuristic adopted deep learning framework for diagnosing breast cancer using thermography images |
| title_short | Efficient hybrid heuristic adopted deep learning framework for diagnosing breast cancer using thermography images |
| title_sort | efficient hybrid heuristic adopted deep learning framework for diagnosing breast cancer using thermography images |
| topic | Breast cancer classification Thermogram images Optimal binary thresholding Weighted fused feature Rock hyraxes dandelion algorithm optimization StackVDRNet |
| url | https://doi.org/10.1038/s41598-025-96827-5 |
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