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Deep Learning-Based Computer-Aided Diagnosis Model for the Identification and Classification of Mammography Images

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Abstract

Cancer of the breast is an illness that has the potential to be fatal for females all over the world. Even with the advancements that have been made in treatment, breast cancer cannot be prevented or cured; however, with early identification, one's life expectancy can be increased. A woman's overall health can be improved, which can add years to her life expectancy, if breast cancer is detected at an earlier stage. Radiological screening is a well-known method that is utilised for cancer prevention and detection in significant amounts. Mammograms have the ability to detect breast cancer as well as tumours that may be present in the breast. Recent study has demonstrated that DL-based CAD models can assist radiologists in establishing automated diagnosis of breast cancer in patients. The DL-based CAD model helps radiologists diagnose breast cancer automatically, according to recent research. DL techniques utilising convolutional neural network have gained interest because to their effectiveness in automating data feature representation and maximising accuracy by merging classification and feature representations. It successfully diagnoses clinical pictures. The research aims to build DL-based breast cancer diagnosis models and to review state-of-the-art ML and DL models for breast cancer diagnosis and classification. The research also examines the performance of the proposed models on the benchmark dataset. Sensitivity, specificity, accuracy, and F-measure measure performance. The experimental results showed that the proposed models are effective compared to modern methods. The proposed models are effective for breast cancer diagnosis and categorization.

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References

  1. Osareh A, Shadgar B. A computer aided diagnosis system for breast cancer. Int J Comput Sci Issues. 2011;8(2):233–40.

    Google Scholar 

  2. Baker AA. Neuro-fuzzy approach to microcalcification contrast enhancement in digitized mammogram images. Int J Multim Appl. 2012;4(5):61–75.

    Google Scholar 

  3. Rabottino G, Mencattini A, Salmeri M, Caselli F, Lojacono R. Performance evaluation of a region growing procedure for mammographic breast lesion identification. Comput Stand Interfaces. 2011;33:128–35.

    Article  Google Scholar 

  4. Harish Kumar N, Amutha S, Ramesh Babu DR. Enhancement of mammographic images using morphology and wavelet transform. Int J Comput Technol Appl. 2012;3(1):192–8.

    Google Scholar 

  5. Juarez, LC, Ponomaryov V, Sanchez RJL (2006) Detection of micro calcifications in digital mammograms images using wavelet transform. In: Proceedings of IEEE electronics, robotics and automotive mechanics conference, vol 2, pp 58–61

  6. Sridhar B, Reddy KVVS (2013) Qualitative detection of breast cancer by morphological curve let transform. In: 8th international conference on computer science & education, pp 514–517

  7. Software as a Medical Device (SaMD). 2019. https://www.fda.gov/medical-devices/digital-health/softwaremedical-device-samd. Accessed 24 July 2022.

  8. SaMD N41. Software as a medical device (SaMD): clinical evaluation. 2017. Available online: http://www.imdrf.org/docs/imdrf/final/technical/imdrf-tech-170921-samd-n41-clinical-evaluation_1.pdf. Accessed 24 July 2022).

  9. Houssein EH, Emam MM, Ali AA, Suganthan PN. Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review. Expert Syst Appl. 2021;167: 114161.

    Article  Google Scholar 

  10. Yi-Chen Li Y-C, Thau-Yun Shen T-Y, Chien-Cheng Chen C-C, Wei-Ting Chang W-T, Po-Yang Lee P-T, Chih-Chung Huang C-C. Automatic detection of atherosclerotic plaque and calcification from intravascular ultrasound Images by using deep convolutional neural networks. IEEE Trans Ultrason Ferroelectr Freq Control. 2021;68:1762–72.

    Article  Google Scholar 

  11. Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M, et al. Tensor flow: a system for large-scale machine learning. In: Proceedings of the 12th USENIX symposium operating systems design and implementation, Savannah, GA, USA, 2–4 November 2016, pp 265–83.

  12. Sharma S, Khanna P. Computer-aided diagnosis of malignant mammograms using Zernike moments and SVM. J Digit Imaging. 2015;28:77–90.

    Article  Google Scholar 

  13. Tsai K-J, Chou M-C, Li H-M, Liu S-T, Hsu J-H, Yeh W-C, Hung C-M, Yeh C-Y, Hwang S-H. A high-performance deep neural network model for BI-RADS classification of screening mammography. Sensors. 2022;22:1160.

    Article  Google Scholar 

  14. Boumaraf S, Liu XB, Ferkous C, Ma XH. A new computer-aided diagnosis system with modified genetic feature selection for BI-RADS classification of breast masses in mammograms. BioMed Res Int. 2020;2020:7695207.

    Article  Google Scholar 

  15. Lin C-H, Wu J-X, Li C-M, Chen P-Y, Pai N-S, Kuo YC. Enhancement of chest X-ray images to improve screening accuracy rate using iterated function system and multilayer fractional-order machine learning classifier. IEEE Photonics J. 2020;12:1–19.

    Google Scholar 

  16. Sarrafa F, Nejadb SH. Improving performance evaluation based on balanced scorecard with grey relational analysis and data envelopment analysis approaches: Case study in water and wastewater companies. Eval Program Plan. 2020;79: 101762.

    Article  Google Scholar 

  17. Thanh DNH, Kalavathi P, Thanh LT, Prasath VBS. Chest X-ray image denoising using Nesterov optimization method with total variation regularization. Procedia Comput Sci. 2020;171:1961–9.

    Article  Google Scholar 

  18. Lin C-H, Wu J-X, Kan C-D, Chen P-Y, Chen W-L. Arteriovenous shunt stenosis assessment based on empirical mode decomposition and 1D convolutional neural network: clinical trial stage. Biomed Signal Process Control. 2021;66: 102461.

    Article  Google Scholar 

  19. Zhang X-H. A convolutional neural network assisted fast tumor screening system based on fractional-order image enhancement: the case of breast X-ray medical imaging. Master’s Thesis, Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung City, Taiwan, 2021.

  20. Maqsood S, Damaševiˇcius R, Maskeliunas R. TTCNN: a breast cancer detection and classification towards computer-aided diagnosis using digital mammography in early stages. Appl Sci. 2022;12:3273.

    Article  Google Scholar 

  21. Suh YJ, Jung J, Cho B-J. Automated breast cancer detection in digital mammograms of various densities via deep learning. J Person Med. 2020;10:211.

    Article  Google Scholar 

  22. Chen P-Y, Sun Z-L, Wu J-X, Pai CC, Li C-M, Lin C-H, Pai N-S. Photoplethysmo graphy analysis with Duffing-Holmes self-synchronization dynamic errors and 1D CNN-based classifier for upper extremity vascular disease screening. Processes. 2021;9:2093.

    Article  Google Scholar 

  23. Lin C-H, Zhang F-Z, Wu J-X, Pai N-S, Chen PY, Pai C-C, Kan C-D. Posteroanterior chest X-ray image classification with a multilayer 1D convolutional neural network based classifier for cardiomegalylevel screening. Electronics. 2022;11:1364.

    Article  Google Scholar 

  24. Chen PY, Zhang X-H, Wu J-X, Pai CC, Hsu J-C, Lin C-H, Pai N-S. Automaticbreast tumor screening of mammographic images with optimal convolutional neural network. Appl Sci. 2022;12:4079.

    Article  Google Scholar 

  25. Vijayarajeswari R, Parthasarathy P, Vivekanandan S, Basha AA. Classification of mammogram for early detection of breast cancer using SVM classifier and Hough transform. Measurement. 2019;146:800–5.

    Article  Google Scholar 

  26. Mahersia H, Boulehmi H, Hamrouni K. Development of intelligent systems based on Bayesian regularization network and neuro-fuzzy models for mass detection in mammograms: a comparative analysis. Comput Methods Prog Biomed. 2016;126:46–62.

    Article  Google Scholar 

  27. Lee J, Nishikawa RM. Automated mammographic breast density estimation using a fully convolutional network. Med Phys. 2018;45:1178–90.

    Article  Google Scholar 

  28. Li S, Dong M, Du G, Mu X. Attention dense-u-net for automatic breast mass segmentation in digital mammogram. IEEE Access. 2019;7:59037–47.

    Article  Google Scholar 

  29. AlGhamdi M, Abdel-Mottaleb M, Collado-Mesa F. Du-net: Convolutional network for the detection of arterial calcifications in mammograms. IEEE Trans Med Imaging. 2020;39:3240–9.

    Article  Google Scholar 

  30. Sarker IH. Machine learning: algorithms, real-world applications and research directions. SN Comput Sci. 2021;2:160.

    Article  Google Scholar 

  31. Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell. 2017;39:640–51.

    Article  Google Scholar 

  32. Xu S, Adeli E, Cheng JZ, Xiang L, Li Y, Lee SW, Shen D. Mammographic mass segmentation using multichannel and multiscale fully convolutional networks. Int J Imaging Syst Technol. 2020;30:1095–107.

    Article  Google Scholar 

  33. Effective mammogram classification based on center symmetric-LBP features in wavelet domain using random forests. Technol Health Care. 2017;25(4):709–27.

  34. Mammogram classification using selected GLCM features and random forest classifier. Int J Comput Sci Inf Secur.

  35. Kalita DJ, Singh VP, Kumar V. Two-way threshold-based intelligent water drops feature selection algorithm for accurate detection of breast cancer. Soft Comput. 2022;26:2277–305.

    Article  Google Scholar 

  36. Kalita DJ, Singh VP, Kumar V. Detection of breast cancer through mammogram using wavelet-based LBP features and IWD feature selection technique. SN Comput Sci. 2022;3:175.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, College of Engineering, Roorkee, India

    Sumit Kumar

  2. Department of IoT, K L Deemed to be University, Vaddeswaram, Guntur, 522302, India

    Bhupati

  3. Department of Computer Science Engineering, VGU University Jaipur, Jaipur, India

    Pawan Bhambu

  4. IBM Department, GLA University, Mathura, India

    Sunita Pachar

  5. Faculty of Human Medicine, Universidad Privada San Juan Bautista, Chincha, Peru

    Juan Carlos Cotrina-Aliaga

  6. Pontificia Universidad Católica del Perú, San Miguel, Peru

    José Luis Arias-Gonzáles

Authors
  1. Sumit Kumar
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  2. Bhupati
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  3. Pawan Bhambu
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  4. Sunita Pachar
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  5. Juan Carlos Cotrina-Aliaga
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  6. José Luis Arias-Gonzáles
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Correspondence to Bhupati.

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This article is part of the topical collection “Machine Intelligence and Smart Systems” guest edited by Manish Gupta and Shikha Agrawal.

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Kumar, S., Bhupati, Bhambu, P. et al. Deep Learning-Based Computer-Aided Diagnosis Model for the Identification and Classification of Mammography Images. SN COMPUT. SCI. 4, 502 (2023). https://doi.org/10.1007/s42979-023-01863-5

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