Diagnostics, Volume 15, Issue 8
2025 April-2 - 108 articles
Cover Story: Breast cancer remains one of the leading causes of cancer-related mortality among women. Early detection through mammography is essential but often limited by variability in radiologist interpretation. This study compares machine learning (ML)-based radiomics and deep learning (DL) approaches for classifying breast lesions. Radiomic features were extracted from 1219 mammograms in the CBIS-DDSM dataset using matRadiomics, while an EfficientNetB6-based DL model was also evaluated. The ML radiomic model achieved area-under-the-curve values of 68.3% for microcalcifications and 61.5% for masses. In contrast, the DL model reached 81.5% and 76.2%, respectively, demonstrating its superior performance and greater potential for accurate breast cancer diagnosis. View this paper - Issues are regarded as officially published after their release is announced to the table of contents alert mailing list .
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