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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.

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Cite This Article

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," Computers, Materials & Continua, vol. 78, no.2, pp. 2441–2468, 2024. https://doi.org/10.32604/cmc.2024.044963



cc 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.
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