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Mamographic image for breast cancer detection and identification of stages of cancer using MFFC and optimized ANFIS

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Abstract

In women, breast cancer (BC) is basically the most often diagnosed cancers. Using mammography, the BC can be diagnosed at a premature stage, and this is the standard technique. The existing paper has posited different mammogram image detection with disparate algorithms; but their accuracy percentage is too low for a medical images since these images are highly affected by different sorts of noise. In addition, only some existing works could recognize the cancer stages. To trounce these issues, a mammographic image for BC detection and identification of stages of cancer utilizing MFFC and OANFIS is proposed. Initially, the image acquisition (IA) process is done on the strength of the morphological opening in addition to closing operation. Then, the adaptive medians filter (AMF) removes the noise as of the mammogram image. Afterward, the edge is preserved; additionally, the contrast is ameliorated as of the noise-eradicated image utilizing anisotropic diffusion histogram equalization (ADHE). Next, the mapping based shifting silhouette (MSS) removes the pectoral part of the contrast-enhanced image. Subsequently, the modified farthest first clustering (MFFC) algorithm clusters the image into (1) normal and (2) abnormal. Then, the Improved region growing (IRG) segment the tumor part from the abnormal image. Later, the features are extracted as of the segmented tumor image. Lastly, as of the extracted features, the OANFIS identifies the tumor stage. The experiments are carried out for analyzing the proposed methods’ performance. The proposed clustering as well as classification algorithms is weighted against the prevailing algorithms concerning some performance measures. The proposed MFFC along with OANFIS exhibits its superiority by attaining the highest accuracy of 99.07 and 99.63%.

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References

  • Akselrod-Ballin A, Chorev M, Shoshan Y, Spiro A, Hazan A, Melamed R, Barkan E et al (2019) Predicting breast cancer by applying deep learning to linked health records and mammograms. Radiology 292(2):331–342

    Article  Google Scholar 

  • Bala Anand M, Karthikeyan N, Karthik S, Sivaparthipan CB (2018) Early detection and prediction of amblyopia by predictive analytics using apache spark. Int Pure Appl Math 119(15):3159–3171

    Google Scholar 

  • Beura S, Majhi B, Dash R, Roy S (2015) Classification of mammogram using two-dimensional discrete orthonormal S-transform for breast cancer detection. Healthc Technol Lett 2(3):46–51

    Article  Google Scholar 

  • Carlos Fiallos B, Pérez MG, Conci A, Andaluz VH (2015) Automatic detection of injuries in mammograms using image analysis techniques. In: International conference on systems, signals and image processing (IWSSIP), IEEE, pp 245–248

  • Dina Ragab A, Sharkas M, Marshall S, Ren J (2019) Breast cancer detection using deep convolutional neural networks and support vector machines. PeerJ 7:e6201

    Article  Google Scholar 

  • Geweid GGN, Abdallah MA (2019) A novel approach for breast cancer investigation and recognition using M-level set-based optimization functions. IEEE Access 7:136343–136357

    Article  Google Scholar 

  • Ghasemzadeh A, Azad SS, Esmaeili E (2019) Breast cancer detection based on Gabor-wavelet transform and machine learning methods. Int J Mach Learn Cybern 10(7):1603–1612

    Article  Google Scholar 

  • Goudarzi M, Maghooli K (2018) Extraction of fuzzy rules at different concept levels related to image features of mammography for diagnosis of breast cancer. Biocybern Biomed Eng 38(4):1004–1014

    Article  Google Scholar 

  • Hamidinekoo A, Denton E, Rampun A, Honnor K, Zwiggelaar R (2018) Deep learning in mammography and breast histology, an overview and future trends. Med Image Anal 47:45–67

    Article  Google Scholar 

  • Htay TT, Maung SS (2018) Early stage breast cancer detection system using GLCM feature extraction and K-nearest neighbor (K-NN) on mammography image. In: 18th international symposium on communications and information technologies (ISCIT), IEEE, pp 171–175

  • Laghmati S, Tmiri A, Cherradi B (2019) Machine learning based system for prediction of breast cancer severity. In: International conference on wireless networks and mobile communications (WINCOM), IEEE, pp 1–5

  • Mao N, Yin P, Wang Q, Liu M, Dong J, Zhang X, Xie H, Hong N (2019) Added value of radiomics on mammography for breast cancer diagnosis: a feasibility study. J Am Coll Radiol 16(4):485–491

    Article  Google Scholar 

  • Mina LM, Isa NAM (2015) Microcalcification diagnosis in digital mammograms based on wavelet analysis and neural networks. In: IEEE international conference on control system, computing and engineering (ICCSCE), IEEE, pp 7–12

  • Nasiri Y, Hariri M, Afzali M (2015) Breast cancer detection in mammograms using wavelet and contourlet transformations. In: 2nd international conference on knowledge-based engineering and innovation (KBEI), IEEE, pp 923–926

  • Pak F, Kanan HR, Alikhassi A (2015) Breast cancer detection and classification in digital mammography based on non-subsampled contourlet transform (NSCT) and super resolution. Comput Methods Progr Biomed 122(2):89–107

    Article  Google Scholar 

  • Platania R, Shams S, Yang S, Zhang J, Lee K, Park S-J (2017) Automated breast cancer diagnosis using deep learning and region of interest detection (bc-droid). In: Proceedings of the 8th ACM international conference on bioinformatics, computational biology, and health informatics, pp 536–543

  • Reethu R, Preetha D, Parameshwaran P, Sivaparthipan CB, Kalaikumaran T (2020) A design of smart device for detection of oral cancer using IoT. Int J Res Eng Sci Manag 3(3):44–47

    Google Scholar 

  • Shen Li, Margolies LR, Rothstein JH, Fluder E, McBride R, Sieh W (2019) Deep learning to improve breast cancer detection on screening mammography. Sci Rep 9(1):1–12

    Google Scholar 

  • Shu X, Zhang L, Wang Z, Lv Q, Yi Z (2020) Deep neural networks with region-based pooling structures for mammographic image classification. IEEE Trans Med Imaging 39(6):2246–2255

    Article  Google Scholar 

  • Soulami KB, Saidi MN, Honnit B, Anibou C, Tamtaoui A (2019) Detection of breast abnormalities in digital mammograms using the electromagnetism-like algorithm. Multimed Tools Appl 78(10):12835–12863

    Article  Google Scholar 

  • Tasdemir SBY, Tasdemir K, Aydin Z (2018) ROI detection in mammogram images using wavelet-based haralick and HOG features. In: 17th IEEE international conference on machine learning and applications (ICMLA), IEEE, pp 105–109

  • Uppal MTN (2016) Classification of mammograms for breast cancer detection using fusion of discrete cosine transform and discrete wavelet transform features. Biomed Res 27(2):322–327

    Google Scholar 

  • Valvano G, Santini G, Martini N, Ripoli A, Iacconi C, Chiappino D, Latta DD (2019) Convolutional neural networks for the segmentation of microcalcification in mammography imaging. J Healthc Eng. https://doi.org/10.1155/2019/9360941

    Article  Google Scholar 

  • Vijayarajeswari R, Parthasarathy P, Vivekanandan S, Basha AA (2019) Classification of mammogram for early detection of breast cancer using SVM classifier and Hough transform. Measurement 146:800–805

    Article  Google Scholar 

  • Wang Z, Ge Yu, Kang Y, Zhao Y, Qu Q (2014) Breast tumor detection in digital mammography based on extreme learning machine. Neurocomputing 128:175–184

    Article  Google Scholar 

  • Wang J, Yang Xi, HongminCai WT, Jin C, Li Li (2016) Discrimination of breast cancer with microcalcifications on mammography by deep learning. Sci Rep 6(1):1–9

    Article  Google Scholar 

  • Wang Z, Li Mo, Wang H, Jiang H, Yao Y, Zhang H, Xin J (2019) Breast cancer detection using extreme learning machine based on feature fusion with CNN deep features. IEEE Access 7:105146–105158

    Article  Google Scholar 

  • Yousefi P (2015) Mammographic image enhancement for breast cancer detection applying wavelet transform. In: IEEE student symposium in biomedical engineering and sciences (ISSBES), IEEE, pp 82–86

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Anna University, Chennai, India

    M. Supriya

  2. Department of Computer Science and Engineering, Ponjesly College of Engineering, Kanyakumari, India

    A. J. Deepa

  3. Department of Electrical and Electronics Engineering, University College of Engineering, Kanyakumari, India

    C. Mythili

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  1. M. Supriya
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  2. A. J. Deepa
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  3. C. Mythili
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Correspondence to M. Supriya.

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Supriya, M., Deepa, A.J. & Mythili, C. Mamographic image for breast cancer detection and identification of stages of cancer using MFFC and optimized ANFIS. J Ambient Intell Human Comput 12, 8731–8745 (2021). https://doi.org/10.1007/s12652-020-02639-y

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  • DOI: https://doi.org/10.1007/s12652-020-02639-y

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