Revolutionizing Disease Detection with U-Net: Enhancing Biomedical Image Segmentation in Healthcare

The growth of the global population and environmental pollution have led to an increased prevalence of diseases, putting significant pressure on medical systems worldwide. Manual detection methods are time-consuming and inefficient, highlighting the need for advanced solutions. U-Net, a robust deep-learning architecture designed for biomedical image segmentation, offers a transformative approach to human disease detection. Capable of generating its input data, the U-Net model requires reasonable training time and demonstrates high robustness, making it stable and suitable for real-world applications. This study explores the fundamental concepts and background of U-Net, emphasizing its practical application in patient care. Drawing from past studies, this paper underscores the urgency of integrating U-Net into healthcare for disease detection. It summarizes the impact and improvements of U-Net on biomedical image classification, identifies challenges in building and training the U-Net architecture, and proposes strategies for implementing this deep learning model in health systems to enhance disease detection.