Abstract
Thermography, being low cost and radiation free, can be the most accessible tool for early detection of breast cancer through mass scanning. But it’s accuracy needs to be improved. Currently, the state-of-the-art classification method in use with thermographic breast cancer detection algorithms serves as a bottleneck due to their limited accuracy and applicability to only a subset of available thermographic breast images with some preconditions. So, a more accurate algorithm applicable to all thermographic breast images without manual intervention is a current need. This article proposes a heuristic algorithm for machine learning algorithms for detecting thermogram breast cancer. The pre-processing of the input thermogram image is accomplished by contrast enhancement, and mean filtering. Then the Gradient Vector Flow Snakes (GVFS) is adopted for breast segmentation. From the segmented images, the entropy-based features are extracted. In the classification phase, a Heuristic-based Neural Network (HNN) is introduced, which diagnoses the breast cancer-affected images. The modification on NN is extended by the Oppositional Improvement-based Tunicate Swarm Algorithm (OI-TSA). Using a benchmark database, the proposed CAD system was evaluated based on accuracy and different performance matrices. The analysis and experimental results showed that our system would contribute to the promising future in the case of breast cancer detection using thermography.
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References
Etehadtavakol, M., Ng, E. Y. K., Chandran, V., & Rabbani, H. (2013). Separable and non-separable discrete wavelet transform based texture features and image classification of breast thermograms. Infrared Physics & Technology, 61, 274–286.
Francis, S. V., & Sasikala, M. (2013). Automatic detection of abnormal breast thermograms using asymmetry analysis of texture features. Journal of Medical Engineering & Technology, 37(1), 17–21.
Bhowmik, M. K., Gogoi, U. R., Das, K., Ghosh, A. K., Bhattacharjee, D., & Majumdar, G. (2016). Standardization of infrared breast thermogram acquisition protocols and abnormality analysis of breast thermograms. In SPIE Commercial + Scientific Sensing and Imaging (pp. 986115-(1–18)).
Gomathi, P., Muniraj, C., & Periasamy, P.S. (2020). Breast thermography based unsupervised anisotropic-feature transformation method for automatic breast cancer detection. Microprocessors and Microsystems, 77.
Gogoi, U. R., Bhowmik, M. K., Bhattacharjee, D., Ghosh, A. K., & Majumdar, G. (2016). A study and analysis of hybrid intelligent techniques for breast cancer detection using breast thermograms. In Hybrid Soft Computing Approaches (pp. 329–359). Springer India.
Alfayez, F., Elsoud, M. A., & Gaber, T. (2020). Thermogram breast cancer detection: A comparative study of two Machine learning techniques. Applied Sciences.
Borchartt, T. B., Resmini, R., & Conci, A. (2011). Thermal feature analysis to aid on breast disease diagnosis. In 21st Brazilian Congress of Mechanical Engineering Natal RN, Brazil. Proceedings of COBEM, ABCM (pp. 24–28).
Bhowmik, M. K., Gogoi, U. R., Majumdar, G., Bhattacharjee, D., Datta, D., & Ghosh, A. K. (2018). Designing of ground-truth-annotated DBT-TU-JU breast thermogram database toward early abnormality prediction. IEEE Journal of Biomedical and Health Informatics, 22(4), 1238–1249.
Acharya, U. R., Ng, E. Y. K., Sree, S. V., Chua, C. K., & Chattopadhyay, S. (2012). Higher order spectra analysis of breast thermograms for the automated identification of breast cancer. Expert Systems.
Krawczyk, B., Schaefer, G., & Wozniak, M. (2015). A hybrid cost-sensitive ensemble for imbalanced breast thermogram classification. Artificial Intelligence in Medicine, 65(3), 219–227.
Fernández-Navarro, Carbonero-Ruz, M., Becerra Alonso, D., & Torres-Jiménez, M. (2017). Global sensitivity estimates for neural network classifiers. IEEE Transactions on Neural Networks and Learning Systems, 28(11), 2592–2604.
Swamy, S. M., Rajakumar, B. R., & Valarmathi, I. R. (2013). Design of hybrid wind and photovoltaic power system using opposition-based genetic algorithm with cauchy mutation. In IET Chennai Fourth International Conference on Sustainable Energy and Intelligent Systems (SEISCON 2013).
Kaur, S., Awasthi, L. K., Sangal, A. L., & Dhiman, G. (2020). Tunicate swarm algorithm: A new bio-inspired based metaheuristic paradigm for global optimization. Engineering Applications of Artificial Intelligence, 90.
Gaber, T., Ismail, G., Anter, A., Soliman, M., Ali, M., Semary, N., Hassanien, A. E., & Snasel, V. (2015). Thermogram breast cancer prediction approach based on neutrosophic sets and fuzzy c-means algorithm. In 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp 4254–4257).
Sathish, D., Kamath, S., Prasad, K., Kadavigere, R., & Martis, R. J. (2016). Asymmetry analysis of breast thermograms using automated segmentation and texture features. Signal, Image and Video Processing, 11, 745–752.
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Suryawanshi, S.P., Dharmani, B.C. (2024). Heuristic Neural Network for Thermography Breast Cancer Detection. In: Kulkarni, A.J., Cheikhrouhou, N. (eds) Intelligent Systems for Smart Cities. ICISA 2023. Springer, Singapore. https://doi.org/10.1007/978-981-99-6984-5_23
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DOI: https://doi.org/10.1007/978-981-99-6984-5_23
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