Enhancements in Breast Cancer Detection and Therapy: Utilizing AI and ML Strategies

Breast cancer is the most common malignancy among women diagnosed worldwide. An early and correct diagnosis is critical for successful treatment and better patient outcomes. This paper explores the growing application of AI and ML techniques in breast cancer diagnosis. We review various studies investigating the efficacy of deep learning models, traditional machine learning algorithms, and novel approaches for tasks like breast cancer classification from histopathological images, risk assessment, lesion segmentation in ultrasound images, and blood-based cancer detection. The reviewed studies show promising results, with accuracy above 90% in some cases. However, limitations such as the need for further validation on diverse datasets, potential computational challenges, and interpretability issues require ongoing research. Overall, the findings suggest that AI and ML hold immense potential in revolutionizing breast cancer diagnosis for improved patient care and clinical practice.