A CNN-based methodology for breast cancer diagnosis using thermal images
| Autor: | Zeina Al Masry, Juan Zuluaga-Gomez, Noureddine Zerhouni, Khaled Benaggoune, Safa Meraghni |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
medicine.medical_specialty Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Biomedical Engineering Computational Mechanics 02 engineering and technology 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Breast cancer FOS: Electrical engineering electronic engineering information engineering 0202 electrical engineering electronic engineering information engineering Medicine Mammography Radiology Nuclear Medicine and imaging medicine.diagnostic_test business.industry Image and Video Processing (eess.IV) Ultrasound Magnetic resonance imaging Electrical Engineering and Systems Science - Image and Video Processing medicine.disease 3. Good health Computer Science Applications Breast thermography 020201 artificial intelligence & image processing Radiology business |
| Zdroj: | Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization. 9:131-145 |
| ISSN: | 2168-1171 2168-1163 |
| Popis: | Micro Abstract: A recent study from GLOBOCAN disclosed that during 2018 two million women worldwide had been diagnosed from breast cancer. This study presents a computer-aided diagnosis system based on convolutional neural networks as an alternative diagnosis methodology for breast cancer diagnosis with thermal images. Experimental results showed that lower false-positives and false-negatives classification rates are obtained when data pre-processing and data augmentation techniques are implemented in these thermal images. Background: There are many types of breast cancer screening techniques such as, mammography, magnetic resonance imaging, ultrasound and blood sample tests, which require either, expensive devices or personal qualified. Currently, some countries still lack access to these main screening techniques due to economic, social or cultural issues. The objective of this study is to demonstrate that computer-aided diagnosis(CAD) systems based on convolutional neural networks (CNN) are faster, reliable and robust than other techniques. Methods: We performed a study of the influence of data pre-processing, data augmentation and database size versus a proposed set of CNN models. Furthermore, we developed a CNN hyper-parameters fine-tuning optimization algorithm using a tree parzen estimator. Results: Among the 57 patients database, our CNN models obtained a higher accuracy (92\%) and F1-score (92\%) that outperforms several state-of-the-art architectures such as ResNet50, SeResNet50 and Inception. Also, we demonstrated that a CNN model that implements data-augmentation techniques reach identical performance metrics in comparison with a CNN that uses a database up to 50\% bigger. Conclusion: This study highlights the benefits of data augmentation and CNNs in thermal breast images. Also, it measures the influence of the database size in the performance of CNNs. 19 pages, 7 figures, 5 tables. Clinical Breast Cancer |
| Databáze: | OpenAIRE |
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