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RETRACTED ARTICLE: Dynamic cross propagation algorithm based detection of micro calcification in digital mammogram

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This article was retracted on 04 July 2022

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Abstract

Micro calcification in mammograms may be considered early signs of breast cancer. However, their detection by a variety of factors is a very challenging task to find cancer in an instant starting stage. A breast compression, more difficult breast anatomy, and in some cases, inaccessible size calculations, as well as the significant variation of low contrast, inherent to mammograms. Therefore a computerized image processing scheme is implemented for detecting early-stage Microcalcification in mammograms. Necessarily the masses and microcalcification may be a prominent early symptom of breast cancer. This proposed Dynamic Cross Propagation Algorithm (DCPA) tested several images from digital database mammography for cancer research and diagnosis. In the first stage of preprocessing will proceed for reducing noise in the original image. In the second stage to Finding of interest, which can be done by using a superior region of interest (SROI) detection and region segmentation, is in the area of a suspicious mass on a mammogram. After detecting suspicious mass on the mammogram, the features like mean, standard deviation, variance, skewness, and entropy values are obtained for further classification. In the Fourth stage the feature result, based classification using the proposed Dynamic Cross Propagation Algorithm (DCPA) compiles to find the loss function in the micro image and compare it with the train the model. In this method, SROI fractal dimensions have computed the value that corresponds with training data to find the suspicious microcalcification regions. The proposed system shows a high rate of true positives and a low rate of false-positive, a superior performance compared to the conventional method. The performance of the proposed DCPA technique is analyzed in terms of precision, recall and F-measure. The experimental results produced a classification that gives better accuracy 98.38% of which indicates that the proposed system is more promising in classification digital mammograms.

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References

  • Baran P, Mayo S, McCormack N, Pacilè S, Tromba G (2018) High-resolution X-ray phase-contrast 3-D imaging of breast tissue specimens as a possible adjunct to histopathology. IEEE Trans Med Imaging 37:12

    Article  Google Scholar 

  • Bellizzi G, Bellizzi GG, Bucci OM, Crocco L, Helbig M, Ley S, Sachs J (2018) Optimization of the working conditions for magnetic nanoparticle-enhanced microwave diagnostics of breast cancer. IEEE Trans Biomed Eng 65(7):1607–1616

    Article  Google Scholar 

  • Bucci OM, Crocco L, Scapaticci R (2015) On the optimal measurement configuration for magnetic nanoparticles-enhanced breast cancer microwave imaging. IEEE Trans Biomed Eng 62(2):407–414

    Article  Google Scholar 

  • Chen D, Huang M, Li W (2019) Knowledge-powered deep breast tumor classification with multiple medical reports. IEEE/ACM Trans Comput Biol Bioinform 1:1–1

    Article  Google Scholar 

  • Eltoukhy MM, Elhoseny M, Hosny KM et al (2018) Computer aided detection of mammographic mass using exact Gaussian-Hermite moments. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-018-0905-1

    Article  Google Scholar 

  • Forouzanfar MH, Foreman KJ, Delossantos AM, Lozano R, Lopez AD, Murray CJ, Naghavi M (2011) Breast and cervical cancer in 187 countries between 1980 and 2010: a systematic analysis. Lancet 378(9801):1461–1484

    Article  Google Scholar 

  • Ganesan K, Acharya U, Chua CK, Min LC, Abraham K, Ng K (2013) Computer-aided breast cancer detection using mammograms: a review. IEEE Rev Biomed Eng 6:77–98

    Article  Google Scholar 

  • Gangadhara S, Barrett-Le P, Nicholson RI, Hiscox A (2012) Pro-metastatic tumor-stroma interactions in breast cancer. Future Oncol 8(11):1427–1442

    Article  Google Scholar 

  • Indra P, Manikandan M (2020) Multilevel Tetrolet transform based breast cancer classifier and diagnosis system for healthcare applications. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-01755-z

    Article  Google Scholar 

  • Johnson JE, Takenaka T, Tanaka T (2008) Two-dimensional time-domain inverse scattering for quantitative analysis of breast composition. IEEE Trans Biomed Eng 55(8):1941–1945

    Article  Google Scholar 

  • Kallenberg M, Petersen K, Nielsen M, Ng AY, Diao P, Igel C, Lillholm M (2016) Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring. IEEE Trans Med Imaging 35(5):1322–1331

    Article  Google Scholar 

  • Liu J, Xu B, Zheng C, Gong Y, Garibaldi J, Soria D (2019) An end-to-end deep learning histochemical scoring system for breast cancer TMA. IEEE Trans Med Imaging 38(2):617–628

    Article  Google Scholar 

  • Loizidou K, Skouroumouni G (2002) A new method for breast micro-calcification detection and characterization using digital temporal subtraction of mammogram pairs. In: IEEE EMBS international conference on biomedical and health informatics (BHI), Conference Location: Chicago, IL, USA, USA

  • Mahrooghy M, Ashraf AB, Daye D, McDonald ES, Rosen M, Mies C, Kontos D (2015) Pharmacokinetic tumor heterogeneity as a prognostic biomarker for classifying breast cancer recurrence risk. IEEE Trans Biomed Eng 62(6):1585–1594

    Article  Google Scholar 

  • Mercan C, Aksoy S, Mercan E, Shapiro LG, Weaver DL, El JG (2018a) Multi-instance multi-label learning for multi-class classification of whole slide breast histopathology images. IEEE Trans Med Imaging 37:1

    Article  Google Scholar 

  • Mercan C, Aksoy S, Mercan E, Shapiro LG, Weaver DL, Elmore JG (2018b) Multi-instance multi-label learning for multi-class classification of whole slide breast histopathology images. IEEE Trans Med Imaging 37:1

    Article  Google Scholar 

  • Mordang JJ, Gubern-Mérida A, Bria A, Tortorella F, Mann RM, Broeders MJM, Karssemeijer N (2017) The importance of early detection of calcifications associated with breast cancer in screening. Breast Cancer Res Treat 167(2):451–458

    Article  Google Scholar 

  • Picco N, Gatenby RA, Anderson ARA (2017) Stem cell plasticity and niche dynamics in cancer progression. IEEE Trans Biomed Eng 64(1):528–537

    Article  Google Scholar 

  • Rampogu S, Baek A, Bavi RS, Son M, Cao GP, Kumar R, Lee KW (2018) Identification of novel scaffolds with dual role as antiepileptic and anti-breast cancer. IEEE/ACM Trans Comput Biol Bioinf 16(5):1–1

    Google Scholar 

  • Rana S, Hampson R, Dobie G (2019) Breast cancer: model reconstruction and image registration from segmented deformed image using visual and force based analysis. IEEE Trans Med Imaging 1:1–11

    Google Scholar 

  • Sainz de Cea MV, Nishikawa RM, Yang Y (2017) Estimating the accuracy level among individual detections in clustered micro calcifications. IEEE Trans Med Imaging 36(5):1162–1171

    Article  Google Scholar 

  • Samala RK, Chan HP, Hadjiiski L, Helvie MA, Richter CD, Kenny HC (2019) Breast cancer diagnosis in digital breast tomosynthesis: effects of training sample size on multi-stage transfer learning using deep neural nets. IEEE Trans Med Imaging 38:3

    Article  Google Scholar 

  • Shin SY, Lee S, Yun ID, Kim SM, Lee KM (2018) Joint weakly and semi-supervised deep learning for localization and classification of masses in breast ultrasound images. IEEE Trans Med Imaging 38:3

    Google Scholar 

  • Suhail Z, Denton ERE, Zwiggelaar R (2018) Classification of micro-calcification in mammograms using scalable linear Fisher discriminant analysis. Med Biol Eng Comput 56(8):1475–1485

    Article  Google Scholar 

  • Tuncay AH, Akduman I (2015) Realistic microwave breast models through T1-weighted 3-D MRI data. IEEE Trans Biomed Eng 62(2):688–698

    Article  Google Scholar 

  • Wu N, Phang J, Park J, Shen Y, Huang Z, Zorin M, Geras KJ (2019) Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Trans Med Imaging 20(11):1–11

    Google Scholar 

  • Xing F, Xie Y, Yang L (2016) An automatic learning-based framework for robust nucleus segmentation. IEEE Trans Med Imaging 35(2):550–566

    Article  Google Scholar 

  • Yee K (2010) Numeral solution of initial boundary value problems involving Maxwell’s equations in isotropic media. IEEE Trans Antennas Propag 14(8):302

    Google Scholar 

  • Yoon H, Zhu YI, Yarmoska SK, Emelianov SY (2018) Design and demonstration of a configurable imaging platform for combined laser, ultrasound, and elasticity imaging. IEEE Trans Med Imaging 38:7

    Google Scholar 

  • Zorin M, Jastrzębski S (2018) Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Trans Med Imaging

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Authors and Affiliations

  1. Department of Information Technology, Chettinad College of Engineering and Technology, Tamil Nadu, Puliyur, Karur District, 639114, India

    S. Sakthi

  2. Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, Erode District, Tamil Nadu, 638 052, India

    P. Balasubramanie

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  1. S. Sakthi
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  2. P. Balasubramanie
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Correspondence to S. Sakthi.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-022-04268-z"

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Sakthi, S., Balasubramanie, P. RETRACTED ARTICLE: Dynamic cross propagation algorithm based detection of micro calcification in digital mammogram. J Ambient Intell Human Comput 12, 5877–5894 (2021). https://doi.org/10.1007/s12652-020-02133-5

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  • DOI: https://doi.org/10.1007/s12652-020-02133-5

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