乳がん：現在の研究

抽象的な

A comparison of Traditional Machine Learning with Early Diagnosis of Breast Cancer

Gonzales Martinez

Breast cancer, a prevalent global health issue, demands timely diagnosis for effective treatment. This article delves into the realm of early breast cancer detection, comparing traditional diagnostic methods with the innovative application of machine learning (ML) techniques. While traditional methods such as mammography and histopathological analysis have been instrumental, ML’s potential to enhance accuracy and efficiency in early diagnosis is gaining prominence. This article evaluates the juxtaposition of these methodologies, highlighting ML’s contributions in image analysis, risk assessment, pathology analysis, data fusion, and pattern recognition. By examining the strengths, challenges, and potential synergies between traditional and ML approaches, this article underscores the evolving landscape of breast cancer diagnosis.