Abstract
Breast cancer is leading disease of females and every year death rate is increased gradually due to the breast cancer. Early diagnosis and treatment of breast cancer is an effective way to reduce the death rate of women. The development of the CAD systems improved the mortality rate by reducing false assumptions. This proposed work presents a computer-aided diagnosis (CAD) system for early detection of tumor in digitized mammograms. A novel classification method for breast lesions is proposed using transformative segmentation and saliency feature block. Experimental results show that proposed methods outperformed the existing method and provide timely diagnosis which greatly reduces the mortality rate in medical informatics.
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Jalalian, A., et al.: Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. Clin. Imaging 37(3), 420–426 (2013)
DeSantis, C.E., et al.: International variation in female breast cancer incidence and mortality rates. Cancer Epidemiol. Biomark. Prev. 24(10), 1495–1506 (2015)
Eltoukhy, M.M.M., Faye, I., Samir, B.B.: Using curvelet transform to detect breast cancer in digital mammogram. In: 5th International Colloquium on Signal Processing & its Applications. CSPA 2009. IEEE (2009)
Starikov, A., et al.: 2D mammography, digital breast tomosynthesis, and ultrasound: which should be used for the different breast densities in breast cancer screening? Clin. Imaging 40(1), 68–71 (2016)
Deng, S., et al.: Radiolabeled cyclic arginine-glycine-aspartic (RGD)-conjugated iron oxide nanoparticles as single-photon emission computed tomography (SPECT) and magnetic resonance imaging (MRI) dual-modality agents for imaging of breast cancer. J. Nanopart. Res. 17(1), 1–11 (2015)
Hildebrandt, M.G., et al.: [18F] Fluorodeoxyglucose (FDG)-Positron Emission Tomography (PET)/Computed Tomography (CT) in Suspected Recurrent Breast Cancer: a prospective comparative study of dual-time-point FDG-PET/CT, contrast-enhanced CT, and bone scintigraphy. J. Clin. Oncol. 34(16), JCO635185 (2016)
McGuire, A., et al.: Effects of age on the detection and management of breast cancer. Cancers 7(2), 908–929 (2015)
Shanmugavadivu, P., Sivakumar, V., Sudhir, R.: Fractal dimension-bound spatio-temporal analysis of digital mammograms. Eur. Phys. J. Spec. Top. 225(1), 137–146 (2016)
Lehman, C.D., et al.: Cancer yield of mammography, MR, and US in high-risk women: prospective multi-institution breast cancer screening study 1. Radiology 244(2), 381–388 (2007)
Oliver, A., et al.: A statistical approach for breast density segmentation. J. Digit. Imaging 23(5), 527–537 (2010)
Oliver, A., et al.: A novel breast tissue density classification methodology. IEEE Trans. Inf Technol. Biomed. 12(1), 55–65 (2008)
Nithya, R., Santhi, B.: Computer aided diagnosis system for mammogram analysis: a survey. J. Med. Imaging Health Informatics 5(4), 653–674 (2015)
Kwok, S.M., et al.: Automatic pectoral muscle segmentation on mediolateral oblique view mammograms. IEEE Trans. Med. Imaging 23(9), 1129–1140 (2004)
Saraswathi, D., Srinivasan, E.: An ensemble approach to diagnose breast cancer using fully complex-valued relaxation neural network classifier. Int. J. Biomed. Eng. Technol. 15(3), 243–260 (2014)
Gedik, N., Atasoy, A., Sevim, Y.: Investigation of wave atom transform by using the classification of mammograms. Appl. Soft Comput. 43, 546–552 (2016)
Wang, Z., et al.: Breast tumor detection in double views mammography based on extreme learning machine. Neural Comput. Appl. 27(1), 227–240 (2016)
Rouhi, R., et al.: Benign and malignant breast tumors classification based on region growing and CNN segmentation. Expert Syst. Appl. 42(3), 990–1002 (2015)
Elangeeran, M., Ramasamy, S., Arumugam, K.: A novel method for benign and malignant characterization of mammographic microcalcifications employing waveatom features and circular complex valued—Extreme Learning Machine. In: 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE (2014)
Tabalvandani, N.S. and K. Faez. Multiple classifier systems for breast mass classification. In: 2014 22nd Iranian Conference on Electrical Engineering (ICEE). IEEE (2014)
Liu, X., Tang, J.: Mass classification in mammograms using selected geometry and texture features, and a new SVM-based feature selection method. IEEE Syst. J. 8(3), 910–920 (2014)
Saki, F., et al.: Fast opposite weight learning rules with application in breast cancer diagnosis. Comput. Biol. Med. 43(1), 32–41 (2013)
Tiedeu, A., et al.: Texture-based analysis of clustered microcalcifications detected on mammograms. Digit. Signal Proc. 22(1), 124–132 (2012)
Zhang, Y., et al.: Building an ensemble system for diagnosing masses in mammograms. Int. J. Comput. Assist. Radiol. Surg. 7(2), 323–329 (2012)
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Mughal, B., Mushtaq, F., Buriro, A. (2020). Classification of Breast Lesions in Combination with Metamorphic Segmentation and Saliency Feature Block. In: Bajwa, I., Sibalija, T., Jawawi, D. (eds) Intelligent Technologies and Applications. INTAP 2019. Communications in Computer and Information Science, vol 1198. Springer, Singapore. https://doi.org/10.1007/978-981-15-5232-8_49
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DOI: https://doi.org/10.1007/978-981-15-5232-8_49
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