ENHANCING BREAST CANCER DIAGNOSIS USING DEEP LEARNING

DEEP LEARNING

In response to the 2022 National Breast Cancer Coalition (NBCC) report revealing nearly 297,790 new cases of invasive breast cancer in women and 2,800 in men, we propose an innovative computational framework for predicting breast cancer subtypes using magnetic resonance imaging (MRI). Our approach involves integrating MRI image profiles across various subtypes and stages of breast cancer to unveil robust patterns associated with Basal-like, Luminal A, Luminal B, and HER2-enriched subtypes. Through advanced computational methodologies, our algorithm aims to achieve highly accurate subtype classification, promising to enhance our understanding of breast cancer heterogeneity and facilitate tailored treatment strategies. The anticipated outcomes of our research are twofold. Firstly, we expect to accurately identify breast cancer subtypes, thereby enabling more targeted and personalized treatment approaches. Secondly, by comprehensively analyzing MRI data, we aim to uncover potential biomarkers that could revolutionize breast cancer diagnosis and treatment efficacy. This study holds significant promise for improving patient outcomes and quality of life by providing clinicians with invaluable insights into breast cancer biology and guiding the development of more effective therapeutic interventions.

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