Abstract
Breast Breast cancer poses a significant global health concern, with approximately 2.2 million new cases and 700,000 deaths reported in 2020. Traditional diagnostic approaches which predominantly depend on expert judgement, have been associated with substantial variability in accuracy. To bridge this gap ML models are used to improve diagnostic out of which the present research investigates the potential of specific machine learning algorithms—D cancer remains one of the most common cancers among women globally, necessitating early detection to improve prognosis and survival rates. Recent advancements in machine learning (ML) have shown promise in enhancing the accuracy and efficiency of breast cancer and lesion detection. This review paper discusses the current methodologies, challenges, and future directions of ML applications in the detection and diagnosis of breast cancer. We analyze various ML algorithms used for analyzing mammograms, ultrasound, and MRI data, evaluating their effectiveness and applicability in clinical settings.
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