• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology

Article

Enhancing Breast Cancer Data Classification Performance in Medical Data by Employing GRU-LMA Deep Learning Classifier for Accurate Diagnosis
  • VIEWS 334
  • PDF 65

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v16i47.2387

Year: 2023, Volume: 16, Issue: 47, Pages: 4490-4503

Original Article

Enhancing Breast Cancer Data Classification Performance in Medical Data by Employing GRU-LMA Deep Learning Classifier for Accurate Diagnosis

S Vibin Chandar1*, V Krishnapriya2

1Research Scholar, Department of Computer Science, Sri Ramakrishna College of Arts & Science, Coimbatore, Tamil Nadu, India
2Associate Professor & Head, Department of Computer Science with Cognitive Systems, Sri Ramakrishna College of Arts & Science, Coimbatore, Tamil Nadu, India

*Corresponding Author
Email: [email protected]

Received Date:19 September 2023, Accepted Date:01 October 2023, Published Date:20 December 2023

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: To suggest a new data classifier to enhance breast cancer data classification in mammography digital images (MDI) in order to reduce the error rate and training time and improve accuracy in breast cancer prediction. A deep learning approach is utilized to identify the significant deep spots in MDI for accurate diagnosis. Methods: The Gated Recurrent Unit (GRU)-based DL approach is employed to speed up the learning phase in the DRNN classifier, and the Levenberg-Marquardt algorithm (LMA) is used to identify and reduce errors during classification. For feature extraction, noise removal, & filtering, Adaptive Median Filtering (AMF) and Tetrolet transform (TTM) algorithms are utilized, and the features are separated into two categories (morphology & texture). The King Abdulaziz University Breast Cancer Mammogram Dataset (KAUBCMD) dataset is used for this research work. KAUBCMD has 1416 case reports, with 2 views for both the right and left breasts, and 5662 MDI with clinical results of 3 target classes (negative, benign, and malignant) and 2 subclasses (incomplete and suspected malignant). MATLAB tool is used to evaluate the DL-based GRU-LMA approach based on performance metrics. The detailed comparative analysis is done with prevailing classifier models such as linear SVM, KNN-HP, and K-Means GRI. Findings: The suggested DL-based GRU-LMA classifier model shows proven results with 95.19% accuracy, 96.03% TPR and 95.09% TNR, 95.07% sensitivity, 97.81% specificity, 96% precision, 97.03% recall, 17 minutes of training time, 3% error rate, 3% FPR, and 4% FNR. GRU-LMA outperforms the current classifier models L-SVM, KNN-HP, and K-Means GRI. Novelty: The proven results show breast cancer prediction and classification accuracy with minimal time and error rate, which helps the radiologist and other BC clinical experts make diagnoses easily. The GRU-LMA addresses the limitations of the prevailing classifier models, L-SVM, KNN-HP, and K-Means GRI.

Keywords: Deep Learning, Recurrent Neural Networks, Classification, Machine Learning, Breast Cancer Prediction, RNN Classifier

References

  1. Iqbal MS, Ahmad W, Alizadehsani R, Hussain S, Rehman R. Breast Cancer Dataset, Classification and Detection Using Deep Learning. Healthcare. 2022;10(12):1–19. Available from: https://doi.org/10.3390/healthcare10122395
  2. Din NMU, Dar RA, Rasool M, Assad A. Breast cancer detection using deep learning: Datasets, methods, and challenges ahead. Computers in Biology and Medicine. 2022;149:106073. Available from: https://doi.org/10.1016/j.compbiomed.2022.106073
  3. Wang X, Ahmad I, Javeed D, Zaidi SA, Alotaibi FM, Ghoneim ME, et al. Intelligent Hybrid Deep Learning Model for Breast Cancer Detection. Electronics. 2022;11(17):1–18. Available from: https://doi.org/10.3390/electronics11172767
  4. Arooj S, Atta-Ur-Rahman, Zubair M, Khan MF, Alissa K, Khan MA, et al. Breast Cancer Detection and Classification Empowered With Transfer Learning. Frontiers in Public Health. 2022;10:1–18. Available from: https://doi.org/10.3389/fpubh.2022.924432
  6. Mohamed EA, Rashed EA, Gaber T, Karam O. Deep learning model for fully automated breast cancer detection system from thermograms. PLOS ONE. 2022;17(1):1–20. Available from: https://doi.org/10.1371/journal.pone.0262349
  7. Hamed G, Marey MAERAE, Amin SESE, Tolba MF. Deep Learning in Breast Cancer Detection and Classification. In: The International Conference on Artificial Intelligence and Computer Vision: Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020) , Advances in Intelligent Systems and Computing. Springer International Publishing. 1153:322–333.
  8. Adam R, Dell’aquila K, Hodges L, Maldjian T, Duong TQ. Deep learning applications to breast cancer detection by magnetic resonance imaging: a literature review. Breast Cancer Research. 2023;25(1):1–12. Available from: https://doi.org/10.1186/s13058-023-01687-4
  9. Nithyanandh S, Jaiganesh V. Dynamic Link Failure Detection using Robust Virus Swarm Routing Protocol in Wireless Sensor Network. International Journal of Recent Technology and Engineering. 2019;8(2):1574–1579. Available from: https://www.ijrte.org/wp-content/uploads/papers/v8i2/B2271078219.pdf
  11. Arooj S, Atta-Ur-Rahman, Zubair M, Khan MF, Alissa K, Khan MA, et al. Breast Cancer Detection and Classification Empowered With Transfer Learning. Frontiers in Public Health. 2022;10:1–18. Available from: https://doi.org/10.3389/fpubh.2022.924432
  12. Humayun M, Khalil MI, Almuayqil SN, Jhanjhi NZ. Framework for Detecting Breast Cancer Risk Presence Using Deep Learning. Electronics. 2023;12(2):1–16. Available from: https://doi.org/10.3390/electronics12020403
  13. Nithyanandh S, Jaiganesh V. Reconnaissance Artificial Bee Colony Routing Protocol to Detect Dynamic Link Failure in Wireless Sensor Network. International Journal of Scientific & Technology Research. 2020;10(10):3244–3251. Available from: https://www.ijstr.org/paper-references.php?ref=IJSTR-1019-23039
  14. Behar N, Shrivastava M. A Novel Model for Breast Cancer Detection and Classification. Engineering, Technology and Applied Science Research. 2022;12(6):9496–9502. Available from: https://etasr.com/index.php/ETASR/article/view/5115
  15. Botlagunta M, Botlagunta MD, Myneni MB, Lakshmi D, Nayyar A, Gullapalli JS, et al. Classification and diagnostic prediction of breast cancer metastasis on clinical data using machine learning algorithms. Scientific Reports. 2023;13(1):1–17. Available from: https://doi.org/10.1038/s41598-023-27548-w
  17. Arooj S, Atta-Ur-Rahman, Zubair M, Khan MF, KA, Khan MA, et al. Breast Cancer Detection and Classification Empowered With Transfer Learning. Frontiers in Public Health. 2022;10:1–18. Available from: https://doi.org/10.3389/fpubh.2022.924432
  18. Egwom OJ, Hassan M, Tanimu JJ, Hamada M, Ogar OM. An LDA–SVM Machine Learning Model for Breast Cancer Classification. BioMedInformatics. 2022;2(3):345–358. Available from: https://doi.org/10.3390/biomedinformatics2030022
  19. Chen H, Wang N, Du X, Mei K, Zhou Y, Cai G. Classification Prediction of Breast Cancer Based on Machine Learning. Computational Intelligence and Neuroscience. 2023;2023:1–9. Available from: https://doi.org/10.1155/2023/6530719
  20. Niknam A, Ghousi F, Masoumi R, Atashi A, Makui A, A. Hybrid Medical Data Mining Model for Identifying Tumor Severity in Breast Cancer Diagnosis. Advances in Industrial Engineering. 2021;55(2):151–164. Available from: https://aie.ut.ac.ir/article_84377_3991e2d869817d530418c7e80cec346d.pdf
  21. Wajeed MA, Tiwari S, Gupta R, Ahmad AJ, Agarwal S, Jamal SS, et al. A Breast Cancer Image Classification Algorithm with 2c Multiclass Support Vector Machine. Journal of Healthcare Engineering. 2023;2023:1–12. Available from: https://doi.org/10.1155/2023/3875525
  22. Alsolami AS, Shalash W, Alsaggaf W, Ashoor S, Refaat H, Elmogy M. King Abdulaziz University Breast Cancer Mammogram Dataset (KAU-BCMD) Data. 2021;6(11):1–15. Available from: https://doi.org/10.3390/data6110111
  23. Nithyanandh S, Omprakash S, Megala D, Karthikeyan MP. Energy Aware Adaptive Sleep Scheduling and Secured Data Transmission Protocol to enhance QoS in IoT Networks using Improvised Firefly Bio-Inspired Algorithm (EAP-IFBA) Indian Journal Of Science And Technology. 2023;16(34):2753–2766. Available from: https://doi.org/10.17485/IJST/v16i34.1706

Copyright

© 2023 Chandar & Krishnapriya. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)

  • 22 December 2023

Year: 2023, Volume: 16, Issue: 47

Article Metrics


Post Graduate Fellowship in Research Communication

More articles

DON'T MISS OUT!

Subscribe now for latest articles and news.