Ensemble Boosted Tree based Mammogram image classification using Texture features and extracted smart features of Deep Neural Network
Abstract This work proposes a technique of breast cancer detection from mammogram images. It is a multistage process which classifies the mammogram images into benign or malignant category. During preprocessing, images of Mammographic Image Analysis Society (MIAS) database are passed through a couple of filters for noise removal, thresholding and cropping techniques to extract the region of interest, followed by augmentation process on database to enhance its size. Features from Deep Convolution Neural Network (DCNN) are merged with texture features to form final feature vector. Using transfer learning, deep features are extracted from a modified DCNN, whose training is performed on 69% of randomly selected images of database from both categories. Features of Grey Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern (LBP) are merged to form texture features. Mean and variance of four parameters (contrast, correlation, homogeneity and entropy) of GLCM are computed in four angular directions, at ten distances. Ensemble Boosted Tree classifier using five-fold cross-validation mode, achieved an accuracy, sensitivity, specificity of 98.8%, 100% and 92.55% respectively on this feature vector.
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Azlan, N. A. N., Lu, C. K., Elamvazuthi, I., & Tang, T. B. (2020). Automatic detection of masses from mammographic images via artificial intelligence techniques. IEEE Sensors Journal, 20(21), 13094-13102.
Boudraa, S., Melouah, A., & Merouani, H. F. (2020). Improving mass discrimination in mammogram-CAD system using texture information and super-resolution reconstruction. Evolving Systems, 11(4), 697-706.
Dibden A, Offman J, Duffy SW, Gabe R. Worldwide Review and Meta-Analysis of Cohort Studies Measuring the Effect of Mammography Screening Programmes on Incidence-Based Breast Cancer Mortality. Cancers. 2020; 12(4):976.
Haralick, R. M., Shanmugam, K., & Dinstein, I. H. (1973). Textural features for image classification. IEEE Transactions on systems, man, and cybernetics, (6), 610-621.
Khan, H. N., Shahid, A. R., Raza, B., Dar, A. H., & Alquhayz, H. (2019). Multi-view feature fusion based four views model for mammogram classification using convolutional neural network. IEEE Access, 7, 165724-165733.
Ko, S., & Lee, Y. (1998). Adaptive center weighted median filter. IEEE Transactions on Circuits and Systems, 38(9), 984-993.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 1097-1105.
Mathur, P., Sathishkumar, K., Chaturvedi, M., Das, P., Sudarshan, K. L., Santhappan, S., ... & ICMR-NCDIR-NCRP Investigator Group. (2020). Cancer statistics, 2020: report from national cancer registry programme, India. JCO Global Oncology, 6, 1063-1075.
Mohanty, F., Rup, S., & Dash, B. (2020). Automated diagnosis of breast cancer using parameter optimized kernel extreme learning machine. Biomedical Signal Processing and Control, 62, 102108.13
Nair, Vinod, and Geoffrey E. Hinton. «Rectified linear units improve restricted boltzmann machines». In Proceedings of the 27th international conference on machine learning (ICML-10), pp. 807-814. 2010.
Ojala, T., Pietikäinen, M., & Harwood, D. (1996). A comparative study of texture measures with classification based on featured distributions. Pattern recognition, 29(1), 51-59.
Omonigho, E. L., David, M., Adejo, A., & Aliyu, S. (2020, March). Breast Cancer: Tumor Detection in Mammogram Images Using Modified AlexNet Deep Convolution Neural Network. In 2020 International Conference in Mathematics, Computer Engineering and Computer Science (ICMCECS) (pp. 1-6). IEEE.
Otsu N. A threshold selection method from gray-level histogram. IEEE Transactions on System Man and Cybemetic. 9(1): 62-66, 1979.
Sannasi Chakravarthy, S. R., & Rajaguru, H. (2020). Detection and classification of microcalcification from digital mammograms with firefly algorithm, extreme learning machine and non?linear regression models: A comparison. International Journal of Imaging Systems and Technology, 30(1), 126-146.
Sha, Z., Hu, L., & Rouyendegh, B. D. (2020). Deep learning and optimization algorithms for automatic breast cancer detection. International Journal of Imaging Systems and Technology, 30(2), 495-506.
Sharma, B. P., & Purwar, R. K. (2020, July). Dual Thresholding based Breast cancer detection in Mammograms. Fourth IEEE World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4) (pp. 589-592).
Song, R., Li, T., & Wang, Y. (2020). Mammographic Classification Based on XGBoost and DCNN With Multi Features. IEEE Access, 8, 75011-75021.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, 15(1), 1929-1958.
Suckling J, Parker J, Dance D, Astley S, Hutt I, Boggis C, et al. The mammographic image analysis society digital mammogram database. Exerpta Medica. 1994; 1069:375–8
Sung, H, Ferlay, J, Siegel, RL, Laversanne, M, Soerjomataram, I, Jemal, A, Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2020.
Young, I. T., & van Vliet, L. J. (1995). Recursive implementation of the Gaussian filter. Signal Processing, 44(2), 139–151. doi:10.1016/0165-1684(95)00020-e.
Zhang, X., Zhang, Y., Han, E. Y., Jacobs, N., Han, Q., Wang, X., & Liu, J. (2018). Classification of whole mammogram and tomosynthesis images using deep convolutional neural networks. IEEE transactions on nanobioscience, 17(3), 237-242.
Boudraa, S., Melouah, A., & Merouani, H. F. (2020). Improving mass discrimination in mammogram-CAD system using texture information and super-resolution reconstruction. Evolving Systems, 11(4), 697-706.
Dibden A, Offman J, Duffy SW, Gabe R. Worldwide Review and Meta-Analysis of Cohort Studies Measuring the Effect of Mammography Screening Programmes on Incidence-Based Breast Cancer Mortality. Cancers. 2020; 12(4):976.
Haralick, R. M., Shanmugam, K., & Dinstein, I. H. (1973). Textural features for image classification. IEEE Transactions on systems, man, and cybernetics, (6), 610-621.
Khan, H. N., Shahid, A. R., Raza, B., Dar, A. H., & Alquhayz, H. (2019). Multi-view feature fusion based four views model for mammogram classification using convolutional neural network. IEEE Access, 7, 165724-165733.
Ko, S., & Lee, Y. (1998). Adaptive center weighted median filter. IEEE Transactions on Circuits and Systems, 38(9), 984-993.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 1097-1105.
Mathur, P., Sathishkumar, K., Chaturvedi, M., Das, P., Sudarshan, K. L., Santhappan, S., ... & ICMR-NCDIR-NCRP Investigator Group. (2020). Cancer statistics, 2020: report from national cancer registry programme, India. JCO Global Oncology, 6, 1063-1075.
Mohanty, F., Rup, S., & Dash, B. (2020). Automated diagnosis of breast cancer using parameter optimized kernel extreme learning machine. Biomedical Signal Processing and Control, 62, 102108.13
Nair, Vinod, and Geoffrey E. Hinton. «Rectified linear units improve restricted boltzmann machines». In Proceedings of the 27th international conference on machine learning (ICML-10), pp. 807-814. 2010.
Ojala, T., Pietikäinen, M., & Harwood, D. (1996). A comparative study of texture measures with classification based on featured distributions. Pattern recognition, 29(1), 51-59.
Omonigho, E. L., David, M., Adejo, A., & Aliyu, S. (2020, March). Breast Cancer: Tumor Detection in Mammogram Images Using Modified AlexNet Deep Convolution Neural Network. In 2020 International Conference in Mathematics, Computer Engineering and Computer Science (ICMCECS) (pp. 1-6). IEEE.
Otsu N. A threshold selection method from gray-level histogram. IEEE Transactions on System Man and Cybemetic. 9(1): 62-66, 1979.
Sannasi Chakravarthy, S. R., & Rajaguru, H. (2020). Detection and classification of microcalcification from digital mammograms with firefly algorithm, extreme learning machine and non?linear regression models: A comparison. International Journal of Imaging Systems and Technology, 30(1), 126-146.
Sha, Z., Hu, L., & Rouyendegh, B. D. (2020). Deep learning and optimization algorithms for automatic breast cancer detection. International Journal of Imaging Systems and Technology, 30(2), 495-506.
Sharma, B. P., & Purwar, R. K. (2020, July). Dual Thresholding based Breast cancer detection in Mammograms. Fourth IEEE World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4) (pp. 589-592).
Song, R., Li, T., & Wang, Y. (2020). Mammographic Classification Based on XGBoost and DCNN With Multi Features. IEEE Access, 8, 75011-75021.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, 15(1), 1929-1958.
Suckling J, Parker J, Dance D, Astley S, Hutt I, Boggis C, et al. The mammographic image analysis society digital mammogram database. Exerpta Medica. 1994; 1069:375–8
Sung, H, Ferlay, J, Siegel, RL, Laversanne, M, Soerjomataram, I, Jemal, A, Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2020.
Young, I. T., & van Vliet, L. J. (1995). Recursive implementation of the Gaussian filter. Signal Processing, 44(2), 139–151. doi:10.1016/0165-1684(95)00020-e.
Zhang, X., Zhang, Y., Han, E. Y., Jacobs, N., Han, Q., Wang, X., & Liu, J. (2018). Classification of whole mammogram and tomosynthesis images using deep convolutional neural networks. IEEE transactions on nanobioscience, 17(3), 237-242.
Sharma, B. P., & Purwar, R. K. (2022). Ensemble Boosted Tree based Mammogram image classification using Texture features and extracted smart features of Deep Neural Network. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 10(4), 419–434. https://doi.org/10.14201/ADCAIJ2021104419434
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