Open Access iconOpen Access

ARTICLE

crossmark

Machine Learning Techniques Using Deep Instinctive Encoder-Based Feature Extraction for Optimized Breast Cancer Detection

Vaishnawi Priyadarshni1, Sanjay Kumar Sharma1, Mohammad Khalid Imam Rahmani2,*, Baijnath Kaushik3, Rania Almajalid2,*

1 Department of Computer Science, Gautam Buddha University, Greater Noida, Uttar Pradesh, India
2 College of Computing and Informatics, Saudi Electronic University, Riyadh, 11673, Saudi Arabia
3 School of Computer Science, Shri Vaishno Devi University, Katra, India

* Corresponding Authors: Mohammad Khalid Imam Rahmani. Email: email; Rania Almajalid. Email: email

Computers, Materials & Continua 2024, 78(2), 2441-2468. https://doi.org/10.32604/cmc.2024.044963

Abstract

Breast cancer (BC) is one of the leading causes of death among women worldwide, as it has emerged as the most commonly diagnosed malignancy in women. Early detection and effective treatment of BC can help save women’s lives. Developing an efficient technology-based detection system can lead to non-destructive and preliminary cancer detection techniques. This paper proposes a comprehensive framework that can effectively diagnose cancerous cells from benign cells using the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM) data set. The novelty of the proposed framework lies in the integration of various techniques, where the fusion of deep learning (DL), traditional machine learning (ML) techniques, and enhanced classification models have been deployed using the curated dataset. The analysis outcome proves that the proposed enhanced RF (ERF), enhanced DT (EDT) and enhanced LR (ELR) models for BC detection outperformed most of the existing models with impressive results.

Keywords


Cite This Article

APA Style
Priyadarshni, V., Sharma, S.K., Rahmani, M.K.I., Kaushik, B., Almajalid, R. (2024). Machine learning techniques using deep instinctive encoder-based feature extraction for optimized breast cancer detection. Computers, Materials & Continua, 78(2), 2441-2468. https://doi.org/10.32604/cmc.2024.044963
Vancouver Style
Priyadarshni V, Sharma SK, Rahmani MKI, Kaushik B, Almajalid R. Machine learning techniques using deep instinctive encoder-based feature extraction for optimized breast cancer detection. Comput Mater Contin. 2024;78(2):2441-2468 https://doi.org/10.32604/cmc.2024.044963
IEEE Style
V. Priyadarshni, S.K. Sharma, M.K.I. Rahmani, B. Kaushik, and R. Almajalid, “Machine Learning Techniques Using Deep Instinctive Encoder-Based Feature Extraction for Optimized Breast Cancer Detection,” Comput. Mater. Contin., vol. 78, no. 2, pp. 2441-2468, 2024. https://doi.org/10.32604/cmc.2024.044963



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 925

    View

  • 330

    Download

  • 0

    Like

Share Link