Abstract
A mortal disease affecting women globally is breast cancer. The survival rate rises in the matter of breast cancer when the presence of mass is identified ahead of time through a mammogram. The mass is obscured in dense breast tissues and hence the sensitivity is limited in the case of mammography. A computer aided diagnosis (CAD) system helps in overcoming the sensitivity issue in mammography but in turn the system is prone to many false positives. The proposed work incorporates the development of automated density-specific models for false positive reduction. A feature-classifier combination that performs the false positive reduction efficiently in each expert model has been identified. Normal and abnormal mammograms of all the four density types from the Image Retrieval in Medical Applications (IRMA) version of the Digital Database for Screening Mammography (DDSM) database have been employed in this work, resulting in the classification accuracy of 96%, 80%, 76% and 88% and false positive rates (FPR) of 0, 0.26, 0.25 and 0.12 for each density-specific mass detection models respectively.
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References
Balali, G.J., Yar, D.D., Dela, V.G.A., Yeboah, E.E., Asumang, P., Akoto, D.J.: Breast cancer: a review of mammography and clinical breast examination for early detection of cancer. OAlib. 7, 1–19 (2020)
Anupama, M.A., Sowmya, V., Soman, K.P.: Breast cancer classification using capsule network with preprocessed histology images. In: International Conference on Communication and Signal Processing, pp. 143–147. IEEE, India (2019)
Mohan, M., Priya, T.L., Nair, L.S.: Fuzzy c-means segmentation on enhanced mammograms using CLAHE and fourth order complex diffusion. In: Proceedings of the Fourth International Conference on Computing Methodologies and Communication, pp. 647–651. IEEE, India (2020)
Kiruthika, K., Vijayan, D., Lavanya, R.: Retrieval driven classification for mammographic masses. In: International Conference on Communication and Signal Processing, pp. 725–729. IEEE, India (2019)
Kriti, Virmani, J., Dey, N., Kumar, V.: PCA-PNN and PCA-SVM based CAD systems for breast density classification. Appl. Intell. Optim. Biol. Med. 96, 159–180 (2020)
Gopakumar, S., Sruthi, K., Krishnamoorthy: Modified level-set for segmenting breast tumor from thermal images. In: International Conference for Convergence in Technology, pp. 1–5. IEEE, India (2018)
Crow, E.D., Astley, S.M., Hulleman, J.: Is there a safety-net effect with computer-aided detection. J. Med. Imaging (Bellingham) 7 (2020)
Dhahbi, S., Barhoumi, W., Kurek, J., Swiderski, B.: False-positive re duction in computer-aided mass detection using mammographic texture analysis and classification. Comput. Methods Programs Biomed. Update. 160, 75–83 (2018)
Soulami, K.B., Kaabouch, N., Saidi, M.N.: Breast cancer: classification of suspicious regions in digital mammograms based on capsule network. Biomed. Sig. Process. Control 76 (2022)
Laishram, R., Rabidas: WDO optimized detection for mammographic masses and its diagnosis: a unified CAD system. Appl. Soft. Comput. 110 (2021)
Chakraborty, J., Midya, A., Rabidas, R.: Computer-aided detection and diagnosis of mammographic masses using multi-resolution analysis of oriented tissue patterns. Expert Syst. Appl. 99, 168–179 (2018)
Diniz, J.O.B., Diniz, P.H.B., Valente, T.L.A., Silva, A.C., Paiva, A.C., Gattass, M.: Detection of mass regions in mammograms by bilateral analysis adapted to breast density using similarity indexes and convolutional neural networks. Comput. Methods Programs Biomed. Update. 156, 191–207 (2018)
Li, Y., Zhang, L., Chen, H., Cheng, L.: Mass detection in mammograms by bilateral analysis using convolution neural network. Comput. Methods Programs Biomed. Update. 195 (2020)
Mughal, B., Muhammad, N., Sharif, M., Rehman, A., Saba, T.: Removal of pectoral muscle based on topographic map and shape-shifting silhouette. BMC Cancer 18 (2018)
Fredenberg, E., Svensson, B., Danielsson, M., Lazzari, B., Cederstrom, B.: Optimization of mammography with respect to anatomical noise. Med. Phys. 7961 (2021)
Bartol, K., Bojanić, D., Pribanić, T., Petković, T., Donoso, Y.D., Mas, J.S.: On the comparison of classic and deep keypoint detector and descriptor methods. In: 11th International Symposium on Image and Signal Processing and Analysis, pp. 64–69. ResearchGate, India (2020)
Dabass, J., Hanmandlu, M., Vig, R.: Formulation of probability-based pervasive information set features and Hanman transform classifier for the categorization of mammograms. SN Appl. Sci. 3(6), 1–17 (2021). https://doi.org/10.1007/s42452-021-04616-2
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Shrinithi, S., Lavanya, R., Vijayan, D. (2023). False Positive Reduction in Mammographic Mass Detection. In: Mercier-Laurent, E., Fernando, X., Chandrabose, A. (eds) Computer, Communication, and Signal Processing. AI, Knowledge Engineering and IoT for Smart Systems. ICCCSP 2023. IFIP Advances in Information and Communication Technology, vol 670. Springer, Cham. https://doi.org/10.1007/978-3-031-39811-7_5
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DOI: https://doi.org/10.1007/978-3-031-39811-7_5
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