Detection of abnormality in breast thermograms using Canny edge detection algorithm for thermography images
by Kumod Kumar Gupta; Ritu Vijay; Pallavi Pahadiya
International Journal of Medical Engineering and Informatics (IJMEI), Vol. 14, No. 1, 2022

Abstract: Currently, research towards cancer is gaining fast attention as methods to cure cancer are a holy grail. Among many potential techniques, breast cancer thermography techniques may come up in saving many lives in the future. The purpose of this paper is to diagnose breast cancer at the preliminary stage using infrared breast thermography. In the first approach, the thermography image is acquired and conclusions are drawn on the basis of their symmetry using the histogram is not appropriate to take decision for the practitioner. In the second approach, the image is processed and applied algorithms to get good result. Further, it also helps us to explore those statistical features that effectively distinguish healthy breast thermograms from that of the thermograms caused by a disease. Finally, graphical representation of the data corresponding to statistical features for both the left and right breast of the healthy and sick patient's breast thermogram has been made in this paper. The mammography report is carefully examined and compared to signify any abnormality. The values obtained from asymmetric analysis based on the abnormality detection system are 94.44% of sensitivity, 83.33% of specificity and 88.88% accuracy. This presented work is fruitful for medical practitioners in early detection of breast cancer.

Online publication date: Wed, 01-Dec-2021

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