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Classification of clustered microcalcifications using different variants of backpropagation training algorithms

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Abstract

In mammography, the most frequently type of breast cancer recognized is DCISand the most frequent signs of DCIS are MCCs. In the proposed research work, MCs are enhanced using fuzzy approach. In this approach Gaussian fuzzy membership function is used and its parameters are optimized by TLBO. After this, the local window based statistical texture features are extracted from ROIs of enhanced mammograms. At the end, different variants of Back propagation are explored to divide MCCs into two categories, one is benign and other is malignant. Here, the main goal is to select an optimal classifier for classifying MCCs as benign or malignant because the performance of CAD system depends on classifier. In this study,the performance of different variants of Back propagationtraining algorithms is not only examined from the accuracy point of view, but also examined from computational point of view. For evaluating the performance of different variants of Back propagation training algorithms, texture features are extracted from mammograms. For experimental results, mammograms of mini-MIAS database are considered.The accuracy is calculated from ROC.88.24% accuracy is achieved by Levenberg-Marquardt training algorithm that is the highest among other variants of Back propagation. Mean Square Error in Levenberg-Marquardt training algorithm case is 3.68e-16 that is the lowest among other variants of Back propagation. Levenberg-Marquardt training algorithm is trained in only 23 iterations for obtaining the above said accuracy. Thus, from experimental results, it is observed that the performance of Levenberg-Marquardt training algorithm is better than other variants of Backpropagation from the accuracy point of view and the computational complexity point of view.

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References

  1. Alam N, Denton ERE, Zwiggelaar R (2019) Classification of microcalcification clusters in digital mammograms using a stack generalization based classifier. Journal of Imaging 5(76):1–24

    Google Scholar 

  2. Arodz T, Kurdziel M, Sevre EOD, Yuen DA (2005) Pattern recognition techniques for automatic detection of suspicious-looking anomalies in mammograms. Comput Methods Prog Biomed 79(2):135–149

    Article  Google Scholar 

  3. Aslan AA, Gultekin S, Yilmaz GK, Kurukahvecioglu O (2021) Is there any association between mammographic features of microcalcifications and breast Cancer subtypes in ductal carcinoma in situ? Acad Radiol 28(7):963–968

    Article  Google Scholar 

  4. Basilea TMA, Fanizzic A, Losurdoc L, Bellottia R, Bottiglid U, Dentamaroc R, Didonnac V, Faustoe A, Massafrac R, Moschettaf M, Tamborrac P, Tangarob S, La Forgiac D (2019) Microcalcification detection in full-field digital mammograms: a fully automated computer-aided system. Physica Medica: European Journal of Medical Physics 64:1–9

    Article  Google Scholar 

  5. Cascio D, Taormina V, Abbene L, Raso G (Nov. 10-17, 2018) A Microcalcification Detection System in Mammograms based on ANN Clustering. Proc. 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC), Sydney, NSW, Australia, pp. 1–4.

  6. Cheng HD, Xu HJ (Aug 16–20, 1998) Fuzzy approach to contrast enhancement. Proc. the 14th IEEE International Conference on Pattern Recognition, Brisbane, Australia, vol. 2, pp. 1549–1551

  7. Cheng HD, Shi XJ, Min R, Hu LM, Cai XP, Du HN (2006) Approaches for automated detection and classification of masses in mammograms. Pattern Recogn 39(4):646–668

    Article  Google Scholar 

  8. Christopher D, Simon P (2020) A novel approach for mammogram enhancement using nonlinear Unsharp masking and L0 gradient minimization. Proc Comput Sci 167:285–292

    Article  Google Scholar 

  9. Cronin KA, Lake AJ, Scott S, Sherman RL, Noone A-M, Howlader N, Henley SJ, Anderson RN, Firth AU, Ma J et al (2018) Annual report to the nation on the status of cancer, Part I: National cancer statistics. Cancer 124(13):2785–2800

    Article  Google Scholar 

  10. Dhawan AP, Buelloni G, Gordon R (1986) Enhancement of mammographic features by optimal adaptive neighborhood image processing. IEEE Trans Med Imaging 5(1):8–15

    Article  Google Scholar 

  11. Fu JC, Lee SK, Wong ST, Yeh JY, Wang AH, Wu HK (2005) Image segmentation features selection and pattern classification for mammographic microcalcifications. Comput Med Imaging Graph 29(6):419–429

    Article  Google Scholar 

  12. Gordon R, Rangayyan RM (1984) Feature enhancement of film mammograms using fixed and adaptive neighborhood. Appl Opt 23(4):560–564

    Article  Google Scholar 

  13. Gulsrud TO, Husoy JH (2001) Optimal filter-based detection of microcalcifications. IEEE Trans Biomed Eng 48(11):1272–1280

    Article  Google Scholar 

  14. Hamid SZ, Farshid RR, Siamak PND (2004) Comparison of multiwavelet, wavelet, Haralick, and shape features for microcalcification classification in mammograms. Pattern Recogn 37(10):1973–1986

    Article  Google Scholar 

  15. Howell A (2010) The merging breast cancer epidemic: early diagnosis and treatment. Breast Cancer Res 12(S4):1–10

    Article  MathSciNet  Google Scholar 

  16. Idokob JB, Abiyevb RH (2017) Machine learning techniques for classification of breast tissue. Proc Comput Sci 120:402–410

    Article  Google Scholar 

  17. Jebathangam J, Shanthi C, Sharmila K, Devi R (May 6-8, 2021) Implementation of fuzzy logic in identification of calcification in mammogram image”, Proc. 2021 IEEE 5th international conference on intelligent computing and control systems (ICICCS), Madurai, India, pp. 806–810

  18. Jenifer S, Parasuraman S (2016) AmudhaKadirvelu, “contrast enhancement and brightness preserving of digital mammograms using fuzzy clipped contrast-limited adaptive histogram equalization algorithm”. Appl Soft Comput 42:167–177

    Article  Google Scholar 

  19. Jia H, Sun K, Song W, Peng X, Lang C, Li Y (2019) Multi-strategy emperor penguin optimizer for RGB histogram-based color satellite image segmentation using Masi entropy. IEEE Open Access J 7:134448–134474

    Article  Google Scholar 

  20. Jiang J, Yao B, Wason AM (2005) Integration of fuzzy logic and structure tensor towards mammogram contrast enhancement. Comput Med Imaging Graph 29(1):83–90

    Article  Google Scholar 

  21. Khairuzzaman AKM, Chaudhury S (2019) Masi entropy based multilevel thresholding for image segmentation. Multimed Tools Appl 78:33573–33591

    Article  Google Scholar 

  22. Kim JK, Park JM, Song KS, Park HW (Sep 24–26, 1997) Texture analysis and artificial neural network for detecting of clustered microcalcifications on mammograms”, Proc. the 7th IEEE Workshop on Neural Networks for Signal Processing, Amelia Island, FL, USA, pp. 199–206

  23. McSweeney MB, Sprawls P, Egan RL (1983) Enhanced image mammography. Am J Roentgenol 140(1):9–14

    Article  Google Scholar 

  24. Mohanalin J, Kalra PK, Kumar N (March 6–7, 2009) Extraction of microcalcifications using non extensive property of mammograms. Proc. IEEE International Conference on Advance Computing (IACC-09), Patiala, Punjab, India, pp. 636–641

  25. Mohanalin J, Kalra PK, Kumar N (Apr 3–5, 2009) Tsallis entropy based contrast enhancement of microcalcifications. Proc. IEEE International Conference on Signal Acquisition and Processing (ICSAP-09), Kuala Lumpur, Malaysia, pp. 3–7

  26. Mohanalin J, Kalra PK, Kumar N (2010) A novel automatic microcalcification detection technique using Tsallis entropy & a type II fuzzy index. Comput Math Appl 60(8):2426–2432

    Article  Google Scholar 

  27. Mohanalin J, Kalra PK, Kumar N (2010) An automatic method to enhance microcalcifications using normalized Tsallis entropy. Signal Process 90(3):952–958

    Article  Google Scholar 

  28. Morrow WM, Paranjape RB, Rangayyan RM, Desautels JEL (1992) Region-based contrast enhancement of mammograms. IEEE Trans Med Imaging 11(3):392–406

    Article  Google Scholar 

  29. Muthuvel M, Thangaraju B, Chinnasamy G (2017) MicrocalciÞcation cluster detection using multiscale products based hessian matrix via the Tsallis thresholding scheme. Pattern Recogn Lett 94(15):127–133

    Article  Google Scholar 

  30. National Cancer Institute Cancer stat facts: female breast cancer. URL: https://seer.cancer.gov/statfacts/html/breast.html

  31. Pal NR, Bhowmick B, Patel SK, Pal S, Das J (2008) A multi-stage neural network aided system for detection of microcalcifications in digitized mammograms. Neurocomputing 71(13–15):2625–2634

    Article  Google Scholar 

  32. Rao RV, More KC (2015) Optimal Design of the Heat Pipe using TLBO (teaching-learning-based optimization) algorithm. Energy 80:535–544

    Article  Google Scholar 

  33. Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315

    Article  Google Scholar 

  34. Ren J, Wang D, Jiang J (2011) Effective recognition of MCC using an improved neural classifier. Eng Appl Artif Intell 24(4):638–645

    Article  Google Scholar 

  35. Saada G, Khadoura A, Kanafan Q (2016) ANN and Adaboost application for automatic detection of microcalcifications in breast cancer. Egypt J Rad Nucl Med 47:1803–1814

    Article  Google Scholar 

  36. Sahu BK, Pati S, Mohanty PK, Panda S (2015) Teaching–learning based optimization algorithm based fuzzy-PID controller for automatic generation control of multi-area power system. Appl Soft Comput 27:240–249

    Article  Google Scholar 

  37. Shachor Y, Greenspan H, Goldberger J (April 8-11, 2019) A mixture of views network with applications to the classification of breast microcalcifications. Proc. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy, pp. 1065–1069

  38. Sheshadri HS, Kandaswamy A (2007) Experimental investigation on breast tissue classification based on statistical feature extraction of mammograms. Comput Med Imaging Graph 31(1):46–48

    Article  Google Scholar 

  39. Sickles E (1986) Breast calcifications: mammographic evaluation. J Radiol 160:289–293

    Article  Google Scholar 

  40. Sujatha K, Durgadevi G, Senthil Kumar K, Karthikeyan V, Ponmagal RS, Hari R, Bhavani NPG, Srividhya V, Cao S-Q (2020) Screening and early identification of microcalcifications in breast using texture-based ANFIS classification”, Advances in ubiquitous sensing applications for healthcare, vol. 7, pp. 115–140.

  41. Sundaram M, Ramar K, Arumugam N, Prabin G (2011) Histogram modified local contrast enhancement for mammogram images. Appl Soft Comput 11(8):5809–5816

    Article  Google Scholar 

  42. Tawani SS, Gurjar AA (Dec 27-28, 2019) A Novel Algorithm for the Automatic Detection and Classification of Microcalcification Clusters Using Wavelets. Proc. 2019 IEEE international conference on innovative trends and advances in engineering and technology (ICITAET), Shegoaon, India, pp. 47–52.

  43. Thurfjell EL, Lernevall KA, Taube AAS (1994) Benefit of independent double reading in a population-based mammography screening program. Radiology 191(1):241–244

    Article  Google Scholar 

  44. Touil A, Kalti K, Conze P-H, Solaiman B, Mahjoub MA (2020) Automatic detection of microcalcification based on morphological operations and structural similarity indices. Biocybern Biomed Eng 40:1155–1173

    Article  Google Scholar 

  45. Tripathy S, Swarnkar T (2020) Unified preprocessing and enhancement technique for mammogram images. Proc Comput Sci 171:1848–1857

    Article  Google Scholar 

  46. Verma B, McLeod P, Klevansky A (2010) Classification of benign and malignant patterns in digital mammogramsfor the diagnosis of breast cancer. Expert Syst Appl 37(4):3344–3351

    Article  Google Scholar 

  47. Wirth MA, Nikitenko D (June 26–28, 2005) Quality evaluation of fuzzy contrast enhancement algorithms. Proc. the Annual Meeting of the North American Fuzzy Information Processing Society, Detroit, MI, USA, USA, pp. 436–441

  48. World Health Organization (WHO) (n.d.) URL: https://www.who.int/cancer/prevention/diagnosis-screening/breast-cancer/en/

  49. Yu S-N, Huang Y-k (2010) Detection of microcalcifications in digital mammograms using combined model-based and statistical textural features. Expert Syst Appl 37(7):5461–5469

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Baba Banda Singh Bahadur Engineering College, Fatehgarh Sahib, Punjab, 140407, India

    Baljit Singh Khehra

  2. Department of Electronics and Communication Engineering, SantLongowal Institute of Engineering and Technology, Longowal, Sangrur, -148106, Punjab, India

    Amar Partap Singh Pharwaha

  3. Computer Engineering Section, Yadvindra College of Engineering, Punjabi University, Guru Kashi Campus, Talwandi Sabo, Punjab, 151302, India

    Balkrishan Jindal

  4. HNC Virtual Solutions, 50430 Pontiac Trail, Wixon, MI, 48393, USA

    Bhupinder Singh Mavi

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  1. Baljit Singh Khehra
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  2. Amar Partap Singh Pharwaha
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  4. Bhupinder Singh Mavi
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Correspondence to Balkrishan Jindal.

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Khehra, B.S., Pharwaha, A.P.S., Jindal, B. et al. Classification of clustered microcalcifications using different variants of backpropagation training algorithms. Multimed Tools Appl 81, 17509–17526 (2022). https://doi.org/10.1007/s11042-022-12017-9

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  • DOI: https://doi.org/10.1007/s11042-022-12017-9

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