Advancing Healthcare Diagnostics

Abstract

This study delves into the unexplored terrain of machine learning–driven digital twins, aiming to revolutionize brain tumor and breast cancer diagnostics while integrating the concept of smart hospitals. Unlike previous research that primarily focused on either brain tumor recognition or breast cancer diagnosis, this investigation pioneers the development of specialized digital twins capable of concurrently addressing both healthcare challenges. Leveraging advanced machine learning models, notably MobileNetV2 with enhanced attention mechanisms, the study meticulously crafts and trains digital twins tailored for MRI image analysis in oncology. These digital replicas undergo rigorous validation before being applied to MRI images for precise classification and diagnostic assessments. The research findings highlight the significant potential of machine learning–enabled digital twins in transforming diagnostic accuracy and treatment strategies within the context of smart hospitals. This pioneering approach not only enhances the capabilities of healthcare providers but also fosters a new era of personalized and efficient healthcare interventions, ultimately contributing to improved patient outcomes and enhanced healthcare delivery

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