乳腺摄影术
乳腺癌
深度学习
人工智能
医学影像学
癌症检测
癌症
医学
乳腺癌筛查
医学物理学
计算机科学
放射科
机器学习
内科学
作者
Shi Gengtian,Bing Bai,Guoyou Zhang
标识
DOI:10.1109/icccs57501.2023.10151156
摘要
Breast cancer is a major health concern affecting women worldwide. Early detection and accurate diagnosis of breast cancer are crucial for improving patient outcomes. In recent years, deep learning techniques have been increasingly applied to medical imaging, including mammography, for the detection and diagnosis of breast cancer. In this study, we proposed a deep learning-based approach using the EfficientNet architecture for the detection and classification of breast cancer. We evaluated the performance of our proposed approach using mammography images from the CBIS-DDSM dataset and achieved accuracy of 0.75 and AUC of 0.83. Our results demonstrate the effectiveness of using deep learning techniques in medical imaging for breast cancer detection and diagnosis.
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