EfficientNet with Hybrid Attention Mechanisms for Enhanced Breast Histopathology Classification: A Comprehensive Approach

• URL: http://arxiv.org/abs/2410.22392v2
• Date: Mon, 04 Nov 2024 09:56:16 GMT
• Title: EfficientNet with Hybrid Attention Mechanisms for Enhanced Breast Histopathology Classification: A Comprehensive Approach
• Authors: Naren Sengodan,
• Abstract summary: This paper introduces a novel approach integrating Hybrid EfficientNet models with advanced attention mechanisms to enhance feature extraction and focus on critical image regions. We evaluate the performance of our models across multiple magnification scales using publicly available hispathology datasets. The results are validated using metrics such as accuracy, F1-score, precision, and recall, demonstrating the clinical potential of our model in improving diagnostic accuracy.
• Score: 0.0
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• Abstract: Breast cancer histopathology image classification is crucial for early cancer detection, offering the potential to reduce mortality rates through timely diagnosis. This paper introduces a novel approach integrating Hybrid EfficientNet models with advanced attention mechanisms, including Convolutional Block Attention Module (CBAM), Self-Attention, and Deformable Attention, to enhance feature extraction and focus on critical image regions. We evaluate the performance of our models across multiple magnification scales using publicly available histopathological datasets. Our method achieves significant improvements, with accuracy reaching 98.42% at 400X magnification, surpassing several state-of-the-art models, including VGG and ResNet architectures. The results are validated using metrics such as accuracy, F1-score, precision, and recall, demonstrating the clinical potential of our model in improving diagnostic accuracy. Furthermore, the proposed method shows increased computational efficiency, making it suitable for integration into real-time diagnostic workflows.

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