Enhancing Early Breast Cancer Detection with Infrared Thermography: A Comparative Evaluation of Deep Learning and Machine Learning Models
Breast cancer remains one of the most prevalent and deadly cancers affecting women worldwide. Early detection is crucial, particularly for younger women, as traditional screening methods like mammography often struggle with accuracy in cases of dense breast tissue. Infrared thermography offers a non...
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MDPI AG
2024-12-01
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| author | Reem Jalloul Chethan Hasigala Krishnappa Victor Ikechukwu Agughasi Ramez Alkhatib |
| author_facet | Reem Jalloul Chethan Hasigala Krishnappa Victor Ikechukwu Agughasi Ramez Alkhatib |
| author_sort | Reem Jalloul |
| collection | DOAJ |
| description | Breast cancer remains one of the most prevalent and deadly cancers affecting women worldwide. Early detection is crucial, particularly for younger women, as traditional screening methods like mammography often struggle with accuracy in cases of dense breast tissue. Infrared thermography offers a non-invasive imaging alternative that enhances early detection by capturing subtle thermal variations indicative of breast abnormalities. This study investigates and compares the performance of various deep learning and machine learning models in analyzing thermographic data to classify breast tissue as healthy, benign, or malignant. To maximize detection accuracy, data preprocessing, feature extraction, and dimensionality reduction were implemented to isolate distinguishing characteristics across tissue types. Leveraging advanced feature extraction and visualization techniques inspired by geospatial data methodologies, we evaluated several deep learning architectures and classical classifiers using the DRM-IR and Breast Thermography Mendeley thermal datasets. Among the tested models, the ResNet152 architecture combined with a Support Vector Machine (SVM) classifier delivered the highest performance, achieving 97.62% accuracy, 95.79% precision, 98.53% recall, 94.52% specificity, an F1 score of 97.16%, an area under the curve (AUC) of 99%, a latency of 0.06 s, and CPU utilization of 88.66%. These findings underscore the potential of integrating infrared thermography with advanced deep learning and machine learning approaches to significantly improve the accuracy and efficiency of breast cancer detection, supporting its role as a valuable tool for early diagnosis. |
| format | Article |
| id | doaj-art-9290f8fe3fac4771a74d705e6bc31978 |
| institution | Kabale University |
| issn | 2227-7080 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Technologies |
| spelling | doaj-art-9290f8fe3fac4771a74d705e6bc319782025-01-24T13:50:43ZengMDPI AGTechnologies2227-70802024-12-01131710.3390/technologies13010007Enhancing Early Breast Cancer Detection with Infrared Thermography: A Comparative Evaluation of Deep Learning and Machine Learning ModelsReem Jalloul0Chethan Hasigala Krishnappa1Victor Ikechukwu Agughasi2Ramez Alkhatib3Maharaja Research Foundation, University of Mysore, Mysuru 570005, IndiaDepartment of Computer Science and Engineering, Maharaja Research Foundation, Maharaja Institute of Technology, Mysuru 571477, IndiaDepartment of Computer Science and Engineering (Artificial Intelligence), Maharaja Institute of Technology, Mysuru 571477, IndiaBiomaterial Bank Nord, Research Center Borstel Leibniz Lung Center, Parkallee 35, 23845 Borstel, GermanyBreast cancer remains one of the most prevalent and deadly cancers affecting women worldwide. Early detection is crucial, particularly for younger women, as traditional screening methods like mammography often struggle with accuracy in cases of dense breast tissue. Infrared thermography offers a non-invasive imaging alternative that enhances early detection by capturing subtle thermal variations indicative of breast abnormalities. This study investigates and compares the performance of various deep learning and machine learning models in analyzing thermographic data to classify breast tissue as healthy, benign, or malignant. To maximize detection accuracy, data preprocessing, feature extraction, and dimensionality reduction were implemented to isolate distinguishing characteristics across tissue types. Leveraging advanced feature extraction and visualization techniques inspired by geospatial data methodologies, we evaluated several deep learning architectures and classical classifiers using the DRM-IR and Breast Thermography Mendeley thermal datasets. Among the tested models, the ResNet152 architecture combined with a Support Vector Machine (SVM) classifier delivered the highest performance, achieving 97.62% accuracy, 95.79% precision, 98.53% recall, 94.52% specificity, an F1 score of 97.16%, an area under the curve (AUC) of 99%, a latency of 0.06 s, and CPU utilization of 88.66%. These findings underscore the potential of integrating infrared thermography with advanced deep learning and machine learning approaches to significantly improve the accuracy and efficiency of breast cancer detection, supporting its role as a valuable tool for early diagnosis.https://www.mdpi.com/2227-7080/13/1/7breast cancer detectiondeep learning architecturesfeature extraction techniquesinfrared thermographymachine learningthermal imaging |
| spellingShingle | Reem Jalloul Chethan Hasigala Krishnappa Victor Ikechukwu Agughasi Ramez Alkhatib Enhancing Early Breast Cancer Detection with Infrared Thermography: A Comparative Evaluation of Deep Learning and Machine Learning Models Technologies breast cancer detection deep learning architectures feature extraction techniques infrared thermography machine learning thermal imaging |
| title | Enhancing Early Breast Cancer Detection with Infrared Thermography: A Comparative Evaluation of Deep Learning and Machine Learning Models |
| title_full | Enhancing Early Breast Cancer Detection with Infrared Thermography: A Comparative Evaluation of Deep Learning and Machine Learning Models |
| title_fullStr | Enhancing Early Breast Cancer Detection with Infrared Thermography: A Comparative Evaluation of Deep Learning and Machine Learning Models |
| title_full_unstemmed | Enhancing Early Breast Cancer Detection with Infrared Thermography: A Comparative Evaluation of Deep Learning and Machine Learning Models |
| title_short | Enhancing Early Breast Cancer Detection with Infrared Thermography: A Comparative Evaluation of Deep Learning and Machine Learning Models |
| title_sort | enhancing early breast cancer detection with infrared thermography a comparative evaluation of deep learning and machine learning models |
| topic | breast cancer detection deep learning architectures feature extraction techniques infrared thermography machine learning thermal imaging |
| url | https://www.mdpi.com/2227-7080/13/1/7 |
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