Enhanced DL-Based Breast Cancer Diagnosis and Classification Using Modified DenseNet-121, DenseNet-201, and MobileNetV2: Optimized Architectures and Refined Activation Functions
Deep learning has revolutionized medical image analysis, particularly in the domain of breast cancer detection. Despite notable progress, further optimization of neural network architectures and activation functions remains critical for enhancing classification accuracy and model generalization. Thi...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11106463/ |
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| Summary: | Deep learning has revolutionized medical image analysis, particularly in the domain of breast cancer detection. Despite notable progress, further optimization of neural network architectures and activation functions remains critical for enhancing classification accuracy and model generalization. This study explores the effect of architectural refinements and activation function choices on the performance of three widely adopted convolutional neural networks: DenseNet-121, DenseNet-201, and MobileNetV2. Specifically, we introduce a modified framework that incorporates selective freezing of early convolutional layers and replaces the conventional ReLU activation function with the smoother GELU activation in the fully connected layers, aiming to improve feature representation and classification robustness. The proposed enhancements are rigorously evaluated across three balanced and widely used benchmark mammography datasets: MIAS, INbreast, and DDSM, using standard performance metrics including Precision, Recall, F1-score, and Accuracy. Experimental findings show that the modified models consistently surpass their original counterparts. Notably, the enhanced DenseNet-201 achieves the highest accuracy of 99.6% on the DDSM dataset. Similarly, the modified DenseNet-121 attains 97% accuracy on MIAS and 98% on INbreast, while the improved DenseNet-201 reaches 98% on both MIAS and INbreast. Furthermore, the lightweight yet optimized MobileNetV2 achieves impressive results, with 99.4% accuracy on DDSM and 99% on INbreast. To validate the statistical significance of these performance gains, paired t-tests were conducted, confirming that the improvements are not only consistent but also statistically meaningful. Our results emphasize the effectiveness of architectural tuning and activation function refinement in advancing deep learning-based breast cancer classification. |
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| ISSN: | 2169-3536 |