Abstract
Ductal carcinoma in situ (DCIS) is a severe breast disease. It generates little symptom and may be neglected in prodromal stage. In this study, we developed a novel DCIS detection method based on breast thermography, which can provide earlier alert than other exams. We created a 40 breast-thermogram dataset. We used six statistical measures, and we used fractal dimension to describe the texture measure. The extreme learning machine was used as the classifier. Our developed system yielded a sensitivity of 93.0 ± 2.6%, a specificity of 92.5 ± 2.6%, and an accuracy of 92.8 ± 1.8%. The extreme learning machine was better than support vector machine, artificial neural network, decision tree, and weighted k-nearest neighbors. Besides, our developed system was superior to six state-of-the-art approaches.
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Acknowledgements
The paper is supported by Open fund for Jiangsu Key Laboratory of Advanced Manufacturing Technology (HGAMTL1601), Natural Science Foundation of China (61602250), Natural Science Foundation of Jiangsu Province (BK20150983), Open fund of Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence (2016CSCI01), Key Laboratory of Measurement and Control of Complex Systems of Engineering, Southeast University, Ministry of Education (MCCSE2017A02).
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Wang, SH., Muhammad, K., Phillips, P. et al. Ductal carcinoma in situ detection in breast thermography by extreme learning machine and combination of statistical measure and fractal dimension. J Ambient Intell Human Comput (2017). https://doi.org/10.1007/s12652-017-0639-5
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DOI: https://doi.org/10.1007/s12652-017-0639-5