Breast cancer detection using thermal images

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Breast cancer is a common disease, accurate and early detection of breast cancer is very important to reduce the mortality and morbidity. Previous studies expose that thermography is a good tool for early detection of the breast cancer. In this paper, a new automatic system will be introduced for the early detection of the breast cancer using thermal images and distinguishing between normal and abnormal breasts. The proposed system is based on combining textural features and histogram of oriented gradients and bag of thermal breast images and then classifying those using three different classifiers: (i) Support vector machine; (ii) Decision tree, and k-Nearest Neighbor. This proposed system provides an automatic classification of the breast cancer using image analysis accurately in low elapsed time. Experimental results showed that cubic SVM has a maximum accuracy of 98.9%, a sensitivity of 98.9%, and a specificity of 99%. When comparing the proposed system with the relevant systems, it’s approved to be more accurate with low elapsed time in learning and testing phase that can help the clinicians in the automatic diagnosis of the breast cancer.

     

     

     


  • Keywords


    Breast Cancer; Support Vector Machine; Thermal Imaging.

  • References


      [1] Doll, R. and R. Peto, The causes of cancer: quantitative estimates of avoidable risks of cancer in the United States today. JNCI: Journal of the National Cancer Institute, 1981. 66(6): p. 1192-1308. https://doi.org/10.1093/jnci/66.6.1192.

      [2] Wolff, A.C., et al., Recommendations for human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline update. Archives of Pathology and Laboratory Medicine, 2014. 138(2): p. 241-256. https://doi.org/10.5858/arpa.2013-0953-SA.

      [3] Kennedy, D.A., T. Lee, and D. Seely, A comparative review of thermography as a breast cancer screening technique. Integrative cancer therapies, 2009. 8(1): p. 9-16. https://doi.org/10.1177/1534735408326171.

      [4] Ring, E. and K. Ammer, The technique of infrared imaging in medicine. Thermology international, 2000. 10(1): p. 7-14.

      [5] Korkolainen, P., Lämmön regeneroinnin hyödyntäminen hydraulipaineakussa. 2014.

      [6] Hankare, P., et al., Breast cancer detection using thermography. Int. Res. J. Eng. Technol, 2016. 4(3): p. 2395-2356.

      [7] Wahab, A.A., et al. Tumor localization in breast thermography with various tissue compositions by using Artificial Neural Network. in 2015 IEEE Student Conference on Research and Development (SCOReD). 2015. IEEE. https://doi.org/10.1109/SCORED.2015.7449383.

      [8] Shahari, S. and A. Wakankar. Color analysis of thermograms for breast cancer detection. in 2015 International Conference on Industrial Instrumentation and Control (ICIC). 2015. IEEE. https://doi.org/10.1109/IIC.2015.7151001.

      [9] Pavithra, P., et al., The effect of thermography on breast cancer detection. Systematic Reviews in Pharmacy, 2018. 9(1): p. 10-16. https://doi.org/10.5530/srp.2018.1.3.

      [10] Acharya, U.R., et al., Thermography based breast cancer detection using texture features and support vector machine. Journal of medical systems, 2012. 36(3): p. 1503-1510. https://doi.org/10.1007/s10916-010-9611-z.

      [11] Duarte, A., et al., Segmentation algorithms for thermal images. Procedia Technology, 2014. 16: p. 1560-1569. https://doi.org/10.1016/j.protcy.2014.10.178.

      [12] Baswaraj, D., A. Govardhan, and P. Premchand, Active contours and image segmentation: The current state of the art. Global Journal of Computer Science and Technology, 2012.

      [13] Ghazali, K.H., et al. Feature extraction technique using discrete wavelet transform for image classification. in 2007 5th Student Conference on Research and Development. 2007. IEEE. https://doi.org/10.1109/SCORED.2007.4451366.

      [14] Minarno, A.E., et al. Texture feature extraction using co-occurrence matrices of sub-band image for batik image classification. in 2014 2nd International Conference on Information and Communication Technology (ICoICT). 2014. IEEE. https://doi.org/10.1109/ICoICT.2014.6914074.

      [15] Shijin, K. and V. Dharun, Extraction of texture features using GLCM and shape features using connected regions. International Journal of Engineering & Technology, 2016. 8(6): p. 2926-2930. https://doi.org/10.21817/ijet/2016/v8i6/160806254.

      [16] Carcagnì, P., et al., Facial expression recognition and histograms of oriented gradients: a comprehensive study. SpringerPlus, 2015. 4(1): p. 645. https://doi.org/10.1186/s40064-015-1427-3.

      [17] Cruz-Roa, A., J.C. Caicedo, and F.A. González, Visual pattern mining in histology image collections using bag of features. Artificial intelligence in medicine, 2011. 52(2): p. 91-106. https://doi.org/10.1016/j.artmed.2011.04.010.

      [18] Thanh Noi, P. and M. Kappas, Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using Sentinel-2 imagery. Sensors, 2018. 18(1): p. 18. https://doi.org/10.3390/s18010018.

      [19] Entezari-Maleki, R., A. Rezaei, and B. Minaei-Bidgoli, Comparison of classification methods based on the type of attributes and sample size. Journal of Convergence Information Technology, 2009. 4(3): p. 94-102. https://doi.org/10.4156/jcit.vol4.issue3.14.

      [20] Carugo, O., F. Eisenhaber, and Carugo, Data mining techniques for the life sciences. 2016: Springer. https://doi.org/10.1007/978-1-4939-3572-7.

      [21] Raschka, S., Python machine learning. 2015: Packt Publishing Ltd.

      [22] Hossin, M. and M. Sulaiman, A review on evaluation metrics for data classification evaluations. International Journal of Data Mining & Knowledge Management Process, 2015. 5(2): p. 1. https://doi.org/10.5121/ijdkp.2015.5201.


 

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Article ID: 30504
 
DOI: 10.14419/ijet.v9i3.30504




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