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Smart IoT in Breast Cancer Detection Using Optimal Deep Learning

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Abstract

IoT in healthcare systems is currently a viable option for providing higher-quality medical care for contemporary e-healthcare. Using an Internet of Things (IoT)–based smart healthcare system, a trustworthy breast cancer classification method called Feedback Artificial Crow Search (FACS)–based Shepherd Convolutional Neural Network (ShCNN) is developed in this research. To choose the best routes, the secure routing operation is first carried out using the recommended FACS while taking fitness measures such as distance, energy, link quality, and latency into account. Then, by merging the Crow Search Algorithm (CSA) and Feedback Artificial Tree, the produced FACS is put into practice (FAT). After the completion of routing phase, the breast cancer categorization process is started at the base station. The feature extraction step is then introduced to the pre-processed input mammography image. As a result, it is possible to successfully get features including area, mean, variance, energy, contrast, correlation, skewness, homogeneity, Gray Level Co-occurrence Matrix (GLCM), and Local Gabor Binary Pattern (LGBP). The quality of the image is next enhanced through data augmentation, and finally, the developed FACS algorithm’s ShCNN is used to classify breast cancer. The performance of FACS-based ShCNN is examined using six metrics, including energy, delay, accuracy, sensitivity, specificity, and True Positive Rate (TPR), with the maximum energy of 0.562 J, the least delay of 0.452 s, the highest accuracy of 91.56%, the higher sensitivity of 96.10%, the highest specificity of 91.80%, and the maximum TPR of 99.45%.

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Data Availability

The datasets generated during and/or analyzed during the current study are available in the Mammographic Image Analysis Society repository, https://www.mammoimage.org/databases/. The datasets generated during and/or analyzed during the current study are available in the Digital Database for Screening Mammography (DDSM) repository, https://www.mammoimage.org/databases/.

References

  1. Shende, Dipali K. and Yogesh S. Angal, and S.C. Patil. “An Iterative CrowWhale-Based Optimization Model for Energy-Aware Multicast Routing in IoT,” International Journal of Information Security and Privacy (IJISP) vol.16, no.1, pp.1–24, 2022.

  2. Shende, Dipali & Sonavane, Shefali & Angal, Yogesh, “A Comprehensive Survey of the Routing Schemes for IoT applications”, Scalable Computing: Practice and Experience, vol.no. 21, pp.203–216, 2020.

  3. Jaglan, P., Dass, R. and Duhan, M., “Breast cancer detection techniques: issues and challenges”, Journal of The Institution of Engineers (India): Series B, vol.100, no.4, pp.379–386, August 2019.

  4. K. Al-Saedi, M. Al-Emran, E. Abusham and S. A. El Rahman, “Mobile Payment Adoption: A Systematic Review of the UTAUT Model,” 2019 International Conference on Fourth Industrial Revolution (ICFIR), 2019, pp. 1–5,2019.

  5. V. Rupapara, M. Narra, N. K. Gunda, S. Gandhi and K. R. Thipparthy, “Maintaining Social Distancing in Pandemic Using Smartphones With Acoustic Waves,” in IEEE Transactions on Computational Social Systems, vol. 9, no. 2, pp. 605–611, 2022,

  6. Gope, P., Gheraibia, Y., Kabir, S. and Sikdar, B., “A secure IoT-based modern healthcare system with fault-tolerant decision making process”, IEEE Journal of Biomedical and Health Informatics, July 2020.

  7. Guo, X., Lin, H., Li, Z. and Peng, M., “Deep-reinforcement-learning-based QoS-aware secure routing for SDN-IoT”, IEEE Internet of Things Journal, vol.7, no.7, pp.6242-6251, December 2019.

    Article  Google Scholar 

  8. Guo, X., Lin, H., Wu, Y. and Peng, M., “A new data clustering strategy for enhancing mutual privacy in healthcare IoT systems”, Future Generation Computer Systems, vol.113, pp.407-417, December 2020.

    Article  Google Scholar 

  9. Reka, S.S. and Dragicevic, T., “Future effectual role of energy delivery: A comprehensive review of Internet of Things and smart grid”, Renewable and Sustainable Energy Reviews, ol.91, pp.90–108, August 2018.

  10. Stojkoska, B.L.R. and Trivodaliev, K.V., “A review of Internet of Things for smart home: Challenges and solutions”, Journal of Cleaner Production, vol.140, pp.1454-1464, January 2017.

    Article  Google Scholar 

  11. Talari, S., Shafie-Khah, M., Siano, P., Loia, V., Tommasetti, A. and Catalão, J.P., “A review of smart cities based on the internet of things concept”, Energies, vol.10, no.4, pp.421, April 2017.

    Article  Google Scholar 

  12. Gope, P. and Hwang, T., “BSN-Care: A secure IoT-based modern healthcare system using body sensor network”, IEEE sensors journal, vol.16, no.5, pp.1368-1376, November 2015.

    Article  Google Scholar 

  13. Botti, Giovanni, “Correction of the naso-jugal groove.” Orbit (Amsterdam, Netherlands) vol. 26,no.3, pp.193–202, 2007.

  14. Mahmoud, M.M., Rodrigues, J.J., Saleem, K., Al-Muhtadi, J., Kumar, N. and Korotaev, V., “Towards energy-aware fog-enabled cloud of things for healthcare”, Computers & Electrical Engineering, vol.67, pp.58-69, April 2018.

    Article  Google Scholar 

  15. Alladi, T. and Chamola, V., “HARCI: A two-way authentication protocol for three entity healthcare IoT networks”, IEEE Journal on Selected Areas in Communications, September 2020.

  16. Mambou, S.J., Maresova, P., Krejcar, O., Selamat, A. and Kuca, K., “Breast cancer detection using infrared thermal imaging and a deep learning model”, Sensors, vol.18, vol.9, pp.2799, September 2018.

  17. Zheng J, Lin D, Gao Z, Wang S, He M, Fan J, “Deep learning assisted efficient AdaBoost algorithm for breast cancer detection and early diagnosis”, IEEE Access, vol.8;8, pp.96946-54, May 2020.

    Article  Google Scholar 

  18. Memon, M.H., Li, J.P., Haq, A.U., Memon, M.H. and Zhou, W., “Breast cancer detection in the IOT health environment using modified recursive feature selection”, wireless communications and mobile computing, November 2019.

  19. Wang, Z., Li, M., Wang, H., Jiang, H., Yao, Y., Zhang, H. and Xin, J., “Breast cancer detection using extreme learning machine based on feature fusion with CNN deep features”, IEEE Access, vol.7, pp.105146-105158, January 2019.

    Article  Google Scholar 

  20. Sun, X., Qian, W. and Song, D., “Ipsilateral-mammogram computer-aided detection of breast cancer”, Computerized Medical Imaging and Graphics, vol.28, no.3, pp.151-158, April 2004.

    Article  PubMed  Google Scholar 

  21. Raposio, Edoardo & Adami, Martina & Capello, Cecilia & Ferrando, Giovanni & Molinari, R & Renzi, M & Caregnato, P & Gualdi, Alessandro & Faggioni, M & Panarese, Paola & Santi, Pierluigi, “Intraoperative expansion of scalp flaps. Quantitative assessment”, Minerva chirurgica, vol.no.55, pp. 629–34, 2000.

  22. Fusini, Federico & Zanchini, Fabio, “Mini-open surgical treatment of an ex professional volleyball player with unresponsive Hoffa’s disease”, Minerva Ortopedica e Traumatologica. vol.no. 67, pp. 192–4, 2016.

  23. Albarqouni, S., Baur, C., Achilles, F., Belagiannis, V., Demirci, S. and Navab, N., “Aggnet: deep learning from crowds for mitosis detection in breast cancer histology images”, IEEE transactions on medical imaging, vol.35, no.5, pp.1313-1321, February 2016.

    Article  PubMed  Google Scholar 

  24. Zhang, Y.D., Satapathy, S.C., Guttery, D.S., Górriz, J.M. and Wang, S.H., “Improved Breast Cancer Classification Through Combining Graph Convolutional Network and Convolutional Neural Network”, Information Processing & Management, vol.58, no.2, pp.102439, March 2021.

    Article  Google Scholar 

  25. Chanak, P. and Banerjee, I., “Congestion free routing mechanism for IoT-enabled wireless sensor networks for smart healthcare applications”, IEEE Transactions on Consumer Electronics, vol.66, no.3, pp.223-232, April 2020.

    Article  Google Scholar 

  26. Awan, K.M., Ashraf, N., Saleem, M.Q., Sheta, O.E., Qureshi, K.N., Zeb, A., Haseeb, K. and Sadiq, A.S., “A priority-based congestion-avoidance routing protocol using IoT-based heterogeneous medical sensors for energy efficiency in healthcare wireless body area networks”, International Journal of Distributed Sensor Networks, vol.15, no.6, pp.1550147719853980, June 2019.

    Article  Google Scholar 

  27. Cicioğlu, M. and Çalhan, A., “SDN‐based wireless body area network routing algorithm for healthcare architecture”, Etri Journal, vol.41, no.4, pp.452-464, August 2019.

    Article  Google Scholar 

  28. Almalki FA, Ben Othman S, A Almalki F, Sakli H., “EERP-DPM: Energy Efficient Routing Protocol Using Dual Prediction Model for Healthcare Using IoT”, Journal of Healthcare Engineering, vol.2021, May 2021.

  29. Kumar, R. and Kumar, D., “Multi-objective fractional artificial bee colony algorithm to energy aware routing protocol in wireless sensor network”, Wireless Networks, vol.22, no.5, pp.1461-1474, 2016.

    Article  Google Scholar 

  30. Ren, J.S., Xu, L., Yan, Q. and Sun, W., “Shepard convolutional neural networks”, In Proceedings of the 28th International Conference on Neural Information Processing System, vol.1, pp.901–909, December 2015.

  31. Askarzadeh, A., “A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm”, Computers & Structures, vol.169, pp.1-12, June 2016.

    Article  Google Scholar 

  32. Li, Q.Q., He, Z.C. and Li, E., “The feedback artificial tree (FAT) algorithm”, Soft Computing, pp.1–28, February 2020.

  33. Varish N, Pal AK., “A novel image retrieval scheme using gray level co-occurrence matrix descriptors of discrete cosine transform based residual image”, Applied Intelligence, vol.48, no.9, pp.2930-53, September 2018.

    Article  Google Scholar 

  34. Zhang, W., Shan, S., Gao, W., Chen, X. and Zhang, H., “Local gabor binary pattern histogram sequence (lgbphs): A novel non-statistical model for face representation and recognition”, In proceedings of Tenth IEEE International Conference on Computer Vision (ICCV’05), vol.1, pp.786–791, October 2005.

  35. MIAS and DDSM Dataset taken from, “https://www.mammoimage.org/databases/”, accessed on May 2021.

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Authors and Affiliations

  1. Department of Information Technology, Vardhaman College of Engineering, Kacharam, Hyderabad, Telangana, India

    Ramachandro Majji

  2. Department of Computational Intelligence, School of Computing, SRM Institute of Science and Technology, Kattangalathur, Chennai, Tamil Nadu, India

    Om Prakash P. G.

  3. Department of Electronics and Communication Engineering, Rajalakshmi Institute of Technology, Chennai, India

    R. Rajeswari

  4. Department of Computer Science and Engineering, GMR Institute of Technology, Rajam, Andhra Pradesh, India

    Cristin R.

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  3. R. Rajeswari
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  4. Cristin R.
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Contributions

Ramachandro Majji conceived the presented idea and designed the analysis. Also, he carried out the experiment and wrote the manuscript with support from Om Prakash P. G. and R. Rajeswari. All authors discussed the results and contributed to the final manuscript. All authors read and approved the final manuscript.

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Correspondence to Ramachandro Majji.

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Majji, R., G., O.P.P., Rajeswari, R. et al. Smart IoT in Breast Cancer Detection Using Optimal Deep Learning. J Digit Imaging 36, 1489–1506 (2023). https://doi.org/10.1007/s10278-023-00834-9

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