Machine Learning Based Breast Cancer Detection and Recognitions Techniques in IoT Environment
Keywords:
Machine learning, IoT, Breast cancer detectionAbstract
Breast cancer is among the worst forms of the disease and one of the major causes of death worldwide. If Breast cancer can be detected and treated before it has spread, it will kill fewer people. Visual inspection is still the best method for diagnosing Breast cancer, despite its flaws. Some researchers believe that deep learning-based technology might help dermatologists detect breast malignancies earlier. Current studies that have used deep learning to categorize Breast cancer are the topic of this literature review. We also detail the most popular DL algorithms and datasets for spotting Breast cancer.
Downloads
References
B. Sahu and V. Manurkar, “Deep Convolutional Neural Network Model for Breast Cancer Prediction,” vol. 12, no. 1, pp. 5075–5084, 2023, doi: 10.48047/ecb/2023.12.1.563.
P. Manikandan, U. Durga, and C. Ponnuraja, “An integrative machine learning framework for classifying SEER breast cancer,” Sci. Rep., vol. 13, no. 1, pp. 1–12, 2023, doi: 10.1038/s41598-023-32029-1.
Alshehri and D. Alsaeed, “Breast Cancer Diagnosis Using Artificial Intelligence Approaches: A Systematic Literature Review,” Intell. Autom. Soft Comput., vol. 37, no. 1, pp. 939–970, 2023, doi: 10.32604/iasc.2023.037096.
Pati et al., “Breast Cancer Diagnosis Based on IoT and Deep Transfer Learning Enabled by Fog Computing,” Diagnostics, vol. 13, no. 13, pp. 1–19, 2023, doi: 10.3390/diagnostics13132191.
Sivasangari, P. Ajitha, Bevishjenila, J. S. Vimali, J. Jose, and S. Gowri, “Breast Cancer Detection Using Machine Learning,” Lect. Notes Data Eng. Commun. Technol., vol. 68, no. July, pp. 693–702, 2022, doi: 10.1007/978-981-16-1866-6_50.
P. Malathi and A. Kalaivani, “A survey on early detection of women’s breast cancer using iot,” Res. Anthol. Med. Informatics Breast Cerv. Cancer, pp. 561–570, 2022, doi: 10.4018/978-1-6684-7136-4.ch029.
N. Behar and M. Shrivastava, “A Novel Model for Breast Cancer Detection and Classification,” Eng. Technol. Appl. Sci. Res., vol. 12, no. 6, pp. 9496–9502, 2022, doi: 10.48084/etasr.5115.
S. Salvi and A. Kadam, “Breast Cancer Detection Using Deep learning and IoT Technologies.,” J. Phys. Conf. Ser., vol. 1831, no. 1, 2021, doi: 10.1088/1742-6596/1831/1/012030.
N. Tariq, “Breast Cancer Detection using Artificial Neural Networks,” J. Mol. Biomark. Diagn., vol. 09, no. 01, 2018, doi: 10.4172/2155-9929.1000371.
S. Ekici and H. Jawzal, "Breast cancer diagnosis using thermography and convolutional neural networks," Medical Hypotheses, vol. 137, Apr. 2020, Art. no. 109542, https://doi.org/10.1016/j.mehy.2019.109542.
D. Singh and A. K. Singh, "Role of image thermography in early breast cancer detection- Past, present and future," Computer Methods and Programs in Biomedicine, vol. 183, Jan. 2020, Art. no. 105074, https://doi.org/10.1016/j.cmpb.2019.105074.
M. Abdar and V. Makarenkov, "CWV-BANN-SVM ensemble learning classifier for an accurate diagnosis of breast cancer," Measurement, vol. 146, pp. 557–570, Nov. 2019, https://doi.org/10.1016/j.measurement. 2019.05.022.
D. A. Omondiagbe, S. Veeramani, and A. S. Sidhu, "Machine Learning Classification Techniques for Breast Cancer Diagnosis," IOP Conference Series: Materials Science and Engineering, vol. 495, Jun. 2019, Art. no. 012033, https://doi.org/10.1088/1757-899X/495/1/ 012033.
P. Jasbi et al., "Breast cancer detection using targeted plasma metabolomics," Journal of Chromatography B, vol. 1105, pp. 26–37, Jan. 2019, https://doi.org/10.1016/j.jchromb.2018.11.029.
M. Swellam, R. F. K. Zahran, H. Abo El-Sadat Taha, N. El-Khazragy, and C. Abdel-Malak, "Role of some circulating MiRNAs on breast cancer diagnosis," Archives of Physiology and Biochemistry, vol. 125, no. 5, pp. 456–464, Oct. 2019, https://doi.org/10.1080/13813455. 2018.1482355
N. Liu, E.-S. Qi, M. Xu, B. Gao, and G.-Q. Liu, "A novel intelligent classification model for breast cancer diagnosis," Information Processing & Management, vol. 56, no. 3, pp. 609–623, May 2019, https://doi.org/10.1016/j.ipm.2018.10.014.
Y. Kumar, S. Gupta, R. Singla, and Y. C. Hu, “A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis,” Arch. Comput. Methods Eng., vol. 29, no. 4, pp. 2043–2070, 2022, doi: 10.1007/s11831-021-09648-w.
M. M. Rahman, Y. Ghasemi, E. Suley, Y. Zhou, S. Wang, and J. Rogers, "Machine Learning Based Computer Aided Diagnosis of Breast Cancer Utilizing Anthropometric and Clinical Features," IRBM, vol. 42, no. 4, pp. 215–226, Aug. 2021, https://doi.org/10.1016/j.irbm.2020.05.005.
R. S. Patil and N. Biradar, "Automated mammogram breast cancer detection using the optimized combination of convolutional and recurrent neural network," Evolutionary Intelligence, vol. 14, no. 4, pp. 1459–1474, Dec. 2021, https://doi.org/10.1007/s12065-020-00403-x.
S. Dalwinder, S. Birmohan, and K. Manpreet, "Simultaneous feature weighting and parameter determination of Neural Networks using Ant Lion Optimization for the classification of breast cancer," Biocybernetics and Biomedical Engineering, vol. 40, no. 1, pp. 337–351, Jan. 2020, https://doi.org/10.1016/j.bbe.2019.12.004.
V. V. Chellam, S. Praveenkumar, S. B. Talukdar, V. Talukdar, S. K. Jain, and A. Gupta, “Development of a Blockchain-based Platform to Simplify the Sharing of Patient Data,” 2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM). IEEE, Feb. 22, 2023. doi: 10.1109/iciptm57143.2023.10118194.
Beltran-Perez, H.-L. Wei, and A. Rubio-Solis, "Generalized Multiscale RBF Networks and the DCT for Breast Cancer Detection," International Journal of Automation and Computing, vol. 17, no. 1, pp. 55–70, Feb. 2020, https://doi.org/10.1007/s11633-019-1210-y.
P. Lalitha Kumari et al., “Methodology for Classifying Objects in High-Resolution Optical Images Using Deep Learning Techniques,” Lecture Notes in Electrical Engineering. Springer Nature Singapore, pp. 619–629, 2023. doi: 10.1007/978-981-19-8865-3_55.
N. Sindhwani et al., “Comparative Analysis of Optimization Algorithms for Antenna Selection in MIMO Systems,” Lecture Notes in Electrical Engineering. Springer Nature Singapore, pp. 607–617, 2023. doi: 10.1007/978-981-19-8865-3_54.
V. Jain, S. M. Beram, V. Talukdar, T. Patil, D. Dhabliya, and A. Gupta, “Accuracy Enhancement in Machine Learning During Blockchain Based Transaction Classification,” 2022 Seventh International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, Nov. 25, 2022. doi: 10.1109/pdgc56933.2022.10053213.
V. Talukdar, D. Dhabliya, B. Kumar, S. B. Talukdar, S. Ahamad, and A. Gupta, “Suspicious Activity Detection and Classification in IoT Environment Using Machine Learning Approach,” 2022 Seventh International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, Nov. 25, 2022. doi: 10.1109/pdgc56933.2022.10053312.
P. R. Kshirsagar, D. H. Reddy, M. Dhingra, D. Dhabliya, and A. Gupta, “A Scalable Platform to Collect, Store, Visualize and Analyze Big Data in Real- Time,” 2023 3rd ICIPTM. IEEE, 2023. doi: 10.1109/iciptm57143.2023.10118183.
M. Dhingra, D. Dhabliya, M. K. Dubey, A. Gupta, and D. H. Reddy, “A Review on Comparison of Machine Learning Algorithms for Text Classification,” 2022 5th International Conference on Contemporary Computing and Informatics (IC3I). IEEE, Dec. 14, 2022. doi: 10.1109/ic3i56241.2022.10072502.
Mandal, K. A. Shukla, A. Ghosh, A. Gupta, and D. Dhabliya, “Molecular Dynamics Simulation for Serial and Parallel Computation Using Leaf Frog Algorithm,” 2022 Seventh International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, Nov. 25, 2022. doi: 10.1109/pdgc56933.2022.10053161.
P. R. Kshirsagar, D. H. Reddy, M. Dhingra, D. Dhabliya, and A. Gupta, “A Review on Application of Deep Learning in Natural Language Processing,” 2022 5th International Conference on Contemporary Computing and Informatics (IC3I). IEEE, Dec. 14, 2022. doi: 10.1109/ic3i56241.2022.10073309.
P. R. Kshirsagar, D. H. Reddy, M. Dhingra, D. Dhabliya, and A. Gupta, “Detection of Liver Disease Using Machine Learning Approach,” 2022 5th International Conference on Contemporary Computing and Informatics (IC3I). IEEE, Dec. 14, 2022. doi: 10.1109/ic3i56241.2022.10073425.
Sahoo, D. K. . (2021). Improved Routing and Secure Data Transmission in Mobile Adhoc Networks Using Trust Based Efficient Randomized Multicast Protocol. Research Journal of Computer Systems and Engineering, 2(2), 06:11. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/25
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Avein Jabar Al- asadi, Taviti Naidu Gongada, Shweta Bandhekar, Pravin B. Waghmare, Ramkumar Venkatasamy, Srinivas Kumar Palvadi, Ankur Gupta
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
