Enhancing breast cancer detection in thermographic images using deep hybrid networks
Vol 6, Issue 2, 2023
VIEWS - 131 (Abstract) 71 (PDF)
Abstract
Breast cancer was a prevalent form of cancer worldwide. Thermography, a method for diagnosing breast cancer, involves recording the thermal patterns of the breast. This article explores the use of a convolutional neural network (CNN) algorithm to extract features from a dataset of thermographic images. Initially, the CNN network was used to extract a feature vector from the images. Subsequently, machine learning techniques can be used for image classification. This study utilizes four classification methods, namely Fully connected neural network (FCnet), support vector machine (SVM), classification linear model (CLINEAR), and KNN, to classify breast cancer from thermographic images. The accuracy rates achieved by the FCnet, SVM, CLINEAR, and k-nearest neighbors (KNN) algorithms were 94.2%, 95.0%, 95.0%, and 94.1%, respectively. Furthermore, the reliability parameters for these classifiers were computed as 92.1%, 97.5%, 96.5%, and 91.2%, while their respective sensitivities were calculated as 95.5%, 94.1%, 90.4%, and 93.2%. These findings can assist experts in developing an expert system for breast cancer diagnosis.
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1. Mousavi H, Bagherian R. Health literacy and breast cancer. Health Psychology. 2019; 8(31): 91-102.
2. Mohamed AA, Berg WA, Peng H, et al. A deep learning method for classifying mammographic breast density categories. Medical Physics. 2018; 45(1): 314-321. doi: 10.1002/mp.12683
3. Clady X, Negri P, Milgram M, Poulenard R. Multi-class vehicle type recognition system. In: Proceedings of the Artificial Neural Networks in Pattern Recognition: Third IAPR Workshop, ANNPR 2008; 2-4 July 2008; Paris, France. pp. 228-239.
4. Desai M, Shah M. An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN). Clinical eHealth. 2021; 4: 1-11. doi: 10.1016/j.ceh.2020.11.002
5. Algehyne EA, Jibril ML, Algehainy NA, et al. Fuzzy Neural Network Expert System with an Improved Gini Index Random Forest-Based Feature Importance Measure Algorithm for Early Diagnosis of Breast Cancer in Saudi Arabia. Big Data and Cognitive Computing. 2022; 6(1): 13. doi: 10.3390/bdcc6010013
6. Aidossov N, Zarikas V, Mashekova A, et al. Evaluation of Integrated CNN, Transfer Learning, and BN with Thermography for Breast Cancer Detection. Applied Sciences. 2023; 13(1): 600. doi: 10.3390/app13010600
7. Riggio AI, Varley KE, Welm AL. The lingering mysteries of metastatic recurrence in breast cancer. British Journal of Cancer. 2020; 124(1): 13-26. doi: 10.1038/s41416-020-01161-4
8. Gonçalves CB, Souza JR, Fernandes H. Classification of static infrared images using pre-trained CNN for breast cancer detection. In: Proceedings of the 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS); 7 June 2021. pp. 101-106.
9. Shahnaz C, Hossain J, Fattah SA, et al. Efficient approaches for accuracy improvement of breast cancer classification using wisconsin database. In: Proceedings of the 2017 IEEE region 10 humanitarian technology conference (R10-HTC); 21 Decemebr 2017. pp. 792-797.
10. Dey S, Roychoudhury R, Malakar S, et al. Screening of breast cancer from thermogram images by edge detection aided deep transfer learning model. Multimedia Tools and Applications. 2022; 81(7): 9331-9349. doi: 10.1007/s11042-021-11477-9
11. DMI: Visual Computing Group. Available online: https://visual.ic.uff.br (accessed on 28 April 2024).
12. Tsietso D, Yahya A, Samikannu R, et al. Multi-Input Deep Learning Approach for Breast Cancer Screening Using Thermal Infrared Imaging and Clinical Data. IEEE Access. 2023; 11: 52101-52116. doi: 10.1109/access.2023.3280422
13. Abdel-Nasser M, Moreno A, Puig D. Breast Cancer Detection in Thermal Infrared Images Using Representation Learning and Texture Analysis Methods. Electronics. 2019; 8(1): 100. doi: 10.3390/electronics8010100
14. Awotunde JB, Panigrahi R, Khandelwal B, et al. Breast cancer diagnosis based on hybrid rule-based feature selection with deep learning algorithm. Research on Biomedical Engineering. 2023; 39(1): 115-127. doi: 10.1007/s42600-022-00255-7
15. Gonçalves CB, Souza JR, Fernandes H. CNN architecture optimization using bio-inspired algorithms for breast cancer detection in infrared images. Computers in Biology and Medicine. 2022; 142: 105205. doi: 10.1016/j.compbiomed.2021.105205
DOI: https://doi.org/10.24294/irr.v6i2.6195
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