Enhanced Breast Cancer Detection and Classification Using CNN-EfficientNet B3: A Deep Learning Approach with Segmentation and Feature Extraction from MIAS Dataset

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 1

Year of Publication : 2025

Authors : K. Punithavathi, G. Yamuna

10.14445/23488549/IJECE-V12I1P120

How to Cite?

K. Punithavathi, G. Yamuna, "Enhanced Breast Cancer Detection and Classification Using CNN-EfficientNet B3: A Deep Learning Approach with Segmentation and Feature Extraction from MIAS Dataset," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 1, pp. 257-270, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I1P120

Abstract:

Breast Cancer (BC), characterized by the uncontrolled proliferation of breast cells, remains the most prevalent and life-threatening cancer affecting women. Early detection significantly increases the chances of survival by enabling timely medical intervention. Numerous methods have been proposed for breast cancer detection; however, limitations in diagnostic accuracy and efficiency persist. To address these challenges, this study introduces a robust deep-learning framework leveraging fine-tuned EfficientNet-B3 for the detection and classification of breast tumors. The methodology employs segmentation techniques to accurately delineate the affected breast tumor regions, reducing training complexity while enhancing classification precision. The model was trained and evaluated using the Mammographic Image Analysis Society (MIAS) dataset, incorporating critical preprocessing steps to optimize image quality and feature extraction. Fine-tuning of EfficientNet-B3 was carried out to adapt the pre-trained network to mammogram-specific features, with hyperparameters optimized for this domain. Performance was assessed using three primary evaluation metrics: accuracy, specificity, and sensitivity. Experimental results demonstrate that the proposed CNN-EfficientNet-B3 framework achieves superior performance compared to conventional approaches, with specificity, accuracy, and sensitivity rates of 97.12%, 96.50%, and 96.43%, respectively. These findings highlight the potential of the proposed method to significantly enhance breast cancer detection and classification, paving the way for more effective clinical applications.

Keywords:

Convolutional Neural Networks, Deep Learning, Breast Cancer, EfficientNet, Mammogram.

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