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An Investigation on Different Approaches for Medical Imaging

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Artificial Intelligence for Sustainable Development

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

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Abstract

The participation of artificial intelligence (AI), particularly in the medical imaging field has enhanced the domains of health innovation. As the lack of condition which is unknown to the affected ones, there is no effective or suitable approaches for preventing and treating the breast cancer. Early detection may increase the possibilities of a full recovery from the disease. A timely analysis of an effective method of identifying and controlling breast cancer. The best method for early breast cancer identification is mammography. This device also makes it possible to identify additional diseases and may reveal details about the type of cancer, such as whether it is normal, malignant, or benign. Basic definitions of concepts like “machine/deep learning” are given in this article, which also examines how AI has been incorporated into radiology. With the advancement of digital imaging technologies, analyzing medical images to diagnose diseases has become increasingly crucial. Clinical medicine can advance through the smart segmentation, identification, and size categorization of breast cancer images using digital image processing technology. This research introduces approaches of medical image identification technology for breast cancer. The investigation of smart segmentation and deep learning for breast cancer is discussed.

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References

  1. Lakhani P, Prater AB, Hutson RK et al (2018) Machine learning in radiology: applications beyond image interpretation. J Am Coll Radiol 15:350–359

    Google Scholar 

  2. Russell S, Bohannon J (2015) Artificial intelligence. Fears of an AI pioneer.Science 349:252

    Google Scholar 

  3. Q. Zhang et al., “Graph neural networks-driven traffic forecasting for connected internet of vehicles,” IEEE Trans. Netw. Sci. Eng., to be published, https://doi.org/10.1109/TNSE.2021.3126830.

  4. K. Yu, L. Tan, X. Shang, J. Huang, G. Srivastava, and P. Chatterjee, “Efficient and privacy-preserving medical research support platform against COVID-19: A blockchain-based approach,” IEEE Consum. Electron. Mag., vol. 10, no. 2, pp. 111–120, Mar. 2021.

    Google Scholar 

  5. H. Li, K. Yu, B. Liu, C. Feng, Z. Qin, and G. Srivastava, “An efficient ciphertext-policy weighted attribute-based encryption for the Internet of Health Things,” IEEE J. Biomed. Health Informat., to be published, https://doi.org/10.1109/JBHI.2021.3075995.

  6. Kalaiselvi Balaraman and S.P.Angelin Claret, “Hybrid Resnet and Bidirectional LSTM-Based Deep Learning Model for Cardiovascular Disease Detection Using PPG Signals, Journal of Machine and Computing, vol.3, no.3, pp. 351-359, July 2023. https://doi.org/10.53759/7669/jmc202303030.

  7. K. Yu, L. Tan, L. Lin, X. Cheng, Z. Yi, and T. Sato, “Deep-learningempowered breast cancer auxiliary diagnosis for 5GB remote E-health,” IEEE Wireless Commun., vol. 28, no. 3, pp. 54–61, Jun. 2021.

    Google Scholar 

  8. P. Gope, Y. Gheraibia, S. Kabir, and B. Sikdar, “A secure IoT-based modern healthcare system with fault-tolerant decision making process,” IEEE J. Biomed. Health Informat., vol. 25, no. 3, pp. 862–873,Mar. 2021

    Google Scholar 

  9. S. Shanthi, S. Saradha, J. A. Smitha, N. Prasath, and H. Anandakumar, “An efficient automatic brain tumor classification using optimized hybrid deep neural network,” International Journal of Intelligent Networks, vol. 3, pp. 188–196, 2022, https://doi.org/10.1016/j.ijin.2022.11.003.

  10. P. Kaur, G. Singh, and P. Kaur, “Intellectual detection and validation of automated mammogram breast cancer images by multi-class SVM using deep learning classification,” Informatics in Medicine Unlocked, vol. 16, Article ID 100151, 2019.

    Google Scholar 

  11. J. Wang, C. Xia, A. Sharma, G. S. Gaba, and M. Shabaz, “Chest CT findings and differential diagnosis of mycoplasma pneumoniae pneumonia and mycoplasma pneumoniae combined with streptococcal pneumonia in children,” Journal of Healthcare Engineering, vol. 2021, pp. 1–10, Article ID 8085530, 2021.

    Google Scholar 

  12. S. S. Mohant y and P. K. Mohant y, “Obesity as Potential Breast Cancer Risk Factor for Postmenopausal Women.” Genes & Diseases, vol. 8, 2019.

    Google Scholar 

  13. H. Wang, B. Zheng, S. W. Yoon, and H. S. Ko, “A support vector machine-based ensemble algorithm for breast cancer diagnosis,” European Journal of Operational Research, vol. 267, no. 2, pp. 687–699, 2018.

    Google Scholar 

  14. M. Tan, B. Zheng, J. K. Leader, and D. Gur, “Association between changes in mammographic image features and risk for near-term breast cancer development,” IEEE Transactions on Medical Imaging, vol. 35, no. 7, pp. 1719–1728, 2016.

    Google Scholar 

  15. A. M. Hemeida, S. Alkhalaf, A. Mady, E. A. Mahmoud, M. E. Hussein, and A. M. B. Eldin, “Implementation of natureinspired optimization algorithms in some data mining tasks,” Ain Shams Engineering Journal, vol. 11, no. (2), 2019.

    Google Scholar 

  16. G. Durgadevi and H. Shekhar, “An intelligent classification of breast cancer images,” Indian Journal of Science and Technology, vol. 9, no. 28, p. 382, 2016.

    Google Scholar 

  17. G.-R. Sinha, “CAD based medical image processing: emphasis to breast cancer detection,” i-Manager’s Journal on Software Engineering, vol. 12, no. 2, p. 15, 2017.

    Google Scholar 

  18. A. Suresh, R. Udendhran, and M. Balamurgan, “A novel internet of things framework integrated with real time monitoring for intelligent healthcare environment,” Journal of MedicalSystems, vol. 43, no. 6, pp. 1–10, 2019.

    Google Scholar 

  19. F. F. Ting, Y. J. Tan, and K. S. Sim, “Convolutional neural network improvement for breast cancer classification,” ExpertSystems with Applications, vol. 120, pp. 103–115, 2019.

    Google Scholar 

  20. S. Chaudhury, N. Shelke, K. Sau, B. Prasanalakshmi, and M. Shabaz, “A novel approach to classifying breast cancer histopathology biopsy images using bilateral knowledge distillation and label smoothing regularization,” in Computational and Mathematical Methods in Medicine, D. Koundal, Ed., vol. 2021, Hindawi Limited, Article ID 4019358, 11 pages, Hindawi Limited, 2021.

    Google Scholar 

  21. W. Wang, C. Qiu, Z. Yin et al., “Blockchain and PUF-based Lightweight Authentication Protocol for Wireless Medical Sensor Networks,” IEEE Internet Of @ings Journal, vol. 1-1, 2021.

    Google Scholar 

  22. R. ‘ippa, S. Bhattacharya, P. K. R. Maddikunta, and S. Hakak, “Antlion re-sampling based deep neural network model for classification of imbalanced multimodal stroke dataset,” Multimed Tools Appl, Springer, Berlin, Germany,2020.

    Google Scholar 

  23. Zheng D, He X, Jing J. Overview of Artificial Intelligence in Breast Cancer Medical Imaging. Journal of Clinical Medicine. 2023; 12(2):419. https://doi.org/10.3390/jcm12020419.

  24. X. Tang, Y. An, and C. Li, “Intelligent Segmentation and Recognition Method of Breast Cancer Based on Digital Image Processing Technology,” Wireless Communications and Mobile Computing, vol. 2021, pp. 1–9, Dec. 2021, https://doi.org/10.1155/2021/2256316.

  25. June Huh Eddie, “A Review of Medical Data Sources, and Advanced Data Analytics in the Medical Sector”, Journal of Biomedical and Sustainable Healthcare Applications, vol.3, no.2, pp. 106-117, July 2023. https://doi.org/10.53759/0088/JBSHA202303011.

  26. R. Arulmurugan and H. Anandakumar, “Region-based seed point cell segmentation and detection for biomedical image analysis,” International Journal of Biomedical Engineering and Technology, vol. 27, no. 4, p. 273, 2018, https://doi.org/10.1504/ijbet.2018.094296.

  27. Kristina Olson, “A Comprehensive Review on Healthcare Data Analytics”, Journal of Biomedical and Sustainable Healthcare Applications, vol.3, no.2, pp. 095-105, July 2023. https://doi.org/10.53759/0088/JBSHA202303010.

  28. V. D. P. Jasti et al., “Computational Technique Based on Machine Learning and Image Processing for Medical Image Analysis of Breast Cancer Diagnosis,” Security and Communication Networks, vol. 2022, pp. 1–7, Mar. 2022, https://doi.org/10.1155/2022/1918379.

  29. C. Militello et al., “On Unsupervised Methods for Medical Image Segmentation: Investigating Classic Approaches in Breast Cancer DCE-MRI,” Applied Sciences, vol. 12, no. 1, p. 162, Dec. 2021, https://doi.org/10.3390/app12010162.

  30. G. Jimenez and D. Racoceanu, “Deep learning for semantic segmentation vs. classification in computational pathology: application to mitosis analysis in breast cancer grading,” Frontiers in Bioengineering and Biotechnology, vol. 7, p. 145, 2019.

    Google Scholar 

  31. S. Shakya, “Analysis of artificial intelligence based image classification techniques,” Journal of Innovative Image Processing (JIIP), vol. 2, no. 1, pp. 44–54, 2020.

    Google Scholar 

  32. H. Zerouaoui and A. Idri, “Reviewing machine learning and image processing based decision-making systems for breast cancer imaging,” Journal of Medical Systems, vol. 45, no. 1,pp. 1–20, 2021.

    Google Scholar 

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Authors and Affiliations

  1. Centre for Future Networks and Digital Twin , Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India

    Anandakumar Haldorai

  2. Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India

    Babitha Lincy R, Suriya Murugan & Minu Balakrishnan

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  1. Anandakumar Haldorai
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  2. Babitha Lincy R
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  4. Minu Balakrishnan
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© 2024 European Alliance for Innovation

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Haldorai, A., R, B.L., Murugan, S., Balakrishnan, M. (2024). An Investigation on Different Approaches for Medical Imaging. In: Artificial Intelligence for Sustainable Development. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-53972-5_3

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  • DOI: https://doi.org/10.1007/978-3-031-53972-5_3

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  • Online ISBN: 978-3-031-53972-5

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