Automatic breast thermography images classification based on deep neural networks

Document Type : Original Article

Author

Department of Basic Science, Faculty of Engineering, Pharos University, Alexandria, Egypt.

Abstract

Breast thermography is a screening tool which is capable of detecting cancer at an early stage. The main objective of this work is using the full power of deep neural network (DNN) and exploring its ability to learn the discriminative features of input data. The transfer learning and data augmentation are performed to solve the problem of lack of labled data. To improve the accuracy, the support vector machine (SVM) classifier will hybrid with the convolutional neural network (CNN) instead of using the deep model as end-to-end. The performance is verified by the k-fold cross-validation. The proposed techniques are trained and evaluated on DMR-IR dataset to classify the thermographic images to normal and abnormal groups. The proposed technique of employing AlexNet hybrid with SVM achieves the best performance, producing 92.55% accuracy, 95.56% sensitivity, 89.80% precision, 92.63% F1 score.

Keywords

References

Brekelmans, C. T., Westers, P., Faber, J. A., Peeters, P. H., & Collette, H. J. (1996). Age specific sensitivity and sojourn time in a breast cancer screening programme (DOM) in The Netherlands: a comparison of different methods. Journal of Epidemiology & Community Health, 50(1), 68-71.
Debi, W. N. D., & Tina, K. (2012). Breast thermography: History, theory, and use. Nat. Med. J, 4.‏
Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009, June). Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition (pp. 248-255). Ieee.‏
DMR, Visual Lab. Database for Mastology Research. Http://Visual.Ic.Uff.Br/ Dmi/, 2017.n.d.
Flusser, J., & Suk, T. (1993). Pattern recognition by affine moment invariants. Pattern recognition, 26(1), 167-174.‏
Hossam, A., Harb, H. M., & Abd El Kader, H. M. (2018). Automatic Image Segmentation Method for Breast Cancer Analysis Using Thermography. Journal of Engineering Sciences, 46(1), 12-32.‏
http://www.globalscientificjournal.com/researchpaper/An_Efficient_and_Reliable_Method_for_Regional_Analysis_of_Breast_Thermographic_Images.pdf
Karim, C. N., Mohamed, O., & Ryad, T. (2018). A new approach for breast abnormality detection based on thermography. Medical Technologies Journal, 2(3), 245-254.‏
Kennedy, D. A., Lee, T., & Seely, D. (2009). A comparative review of thermography as a breast cancer screening technique. Integrative cancer therapies, 8(1), 9-16.‏
Keyserlingk, J. R., Ahlgren, P. D., Yu, E., Belliveau, N., & Yassa, M. (2000). Functional infrared imaging of the breast. IEEE Engineering in Medicine and Biology Magazine, 19(3), 30-41.‏
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90.‏
Lee, M. Y., & Yang, C. S. (2010). Entropy-based feature extraction and decision tree induction for breast cancer diagnosis with standardized thermograph images. Computer methods and programs in biomedicine, 100(3), 269-282.
Mazhar B.Tayel, Azza M.Elbagoury. 2020. “An Efficient and Reliable Method for Regional Analysis of Breast Thermographic Images.” Global Scientific Journal 8 (9): 1508–18.
Ng, E. K. (2009). A review of thermography as promising non-invasive detection modality for breast tumor. International Journal of Thermal Sciences, 48(5), 849-859.‏
Pramanik, S., Bhattacharjee, D., & Nasipuri, M. (2016, September). Texture analysis of breast thermogram for differentiation of malignant and benign breast. In 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 8-14). IEEE.‏
Sathish, D., Kamath, S., Prasad, K., Kadavigere, R., & Martis, R. J. (2017). Asymmetry analysis of breast thermograms using automated segmentation and texture features. Signal, Image and Video Processing, 11(4), 745-752.
Siegel, Rebecca L, Kimberly D Miller, and Ahmedin Jemal. 2019. “Cancer Statistics, 2019.” CA: A Cancer Journal for Clinicians 69 (1): 7–34
Silva, L. F., Saade, D. C. M., Sequeiros, G. O., Silva, A. C., Paiva, A. C., Bravo, R. S., & Conci, A. (2014). A new database for breast research with infrared image. Journal of Medical Imaging and Health Informatics, 4(1), 92-100.‏
Stone, M. (1974). Cross‐validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B (Methodological), 36(2), 111-133.‏
Sun, C., Shrivastava, A., Singh, S., & Gupta, A. (2017). Revisiting unreasonable effectiveness of data in deep learning era. In Proceedings of the IEEE international conference on computer vision (pp. 843-852.
Tayel, M. B., & Elbagoury, A. M. (2020). Breast Infrared Thermography Segmentation Based on Adaptive Tuning of a Fully Convolutional Network. Current Medical Imaging, 16(5), 611-621.‏
Woloshin, S., & Schwartz, L. M. (2010). The benefits and harms of mammography screening: understanding the trade-offs. Jama, 303(2), 164-165.‏
Wong, S. C., Gatt, A., Stamatescu, V., & McDonnell, M. D. (2016, November). Understanding data augmentation for classification: when to warp?. In 2016 international conference on digital image computing: techniques and applications (DICTA)(pp. 1-6). IEEE.‏
Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks?. In Advances in neural information processing systems (pp. 3320-3328).‏
Annals of Optimization Theory and Practice
Volume 3, Issue 2
November 2020
Pages 71-79

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