Breast Cancer Detection Using Transfer Learning
Keywords:
Breast cancer, early detection, risk factors, societal stigmas, gender disparities, pathology, biochemical influences, CBIS-DDSM, SEER, BreakHis, deep learning, MobileNetV2, transfer learning, accuracy, malignant, benign, precision, recall, , f1-score, diagnostic tools, medical researchAbstract
By illuminating the complex interactions between societal stigmas and gender inequities that frequently obstruct early diagnosis, this research constitutes a critical first step towards resolving the difficulties associated with breast cancer detection. Through a comprehensive analysis of risk factors, which encompasses the subtle impacts of biochemical pathways and underlying pathology, the study leverages the abundance of data found in datasets like CBIS-DDSM, SEER, and BreakHis to provide invaluable insights into breast cancer imaging. The research uses deep learning approaches, notably the MobileNetV2 architecture with transfer learning, and is a pioneer in the integration of cutting-edge technology. The results provide a respectable degree of precision in differentiating between benign and malignant cases, even with the intrinsic complexity shown in the 0.616 total accuracy score. A balanced f1-score and notable precision strengths for benign situations highlight the model's potential use in clinical settings. By highlighting the revolutionary potential of deep learning in improving diagnostic tools and changing the landscape of breast cancer detection, this research lays a solid platform for future developments.
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