Optimizing Temporal Features in Ultrasound Imaging: A Multi-Channel Deep Learning Approach for Improved Differentiation of Breast Lesions

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Breast cancer is the most common cancer among women, causing approximately 685,000 deaths annually worldwide.
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Medical ultrasound is the most widely used imaging technique for breast cancer screening due to its cost-effectiveness and non-invasive nature.
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Conventional machine learning approaches for breast lesion differentiation often lack effective feature selection, limiting their classification accuracy.
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Deep learning frameworks have shown improved performance in breast lesion differentiation by integrating multi-channel inputs and automatic feature learning.
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Despite advancements, both conventional and deep learning methods still face challenges in capturing the temporal dynamics of breast lesions.

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