Advances in U-Net and image processing: a path to early cancer diagnosis

Early and accurate diagnosis is the most critical step in the successful treatment of cancer. In contemporary medical image analysis, combining image processing methods with the U-Net deep learning model offers robust assistance to clinicians. Pre-processing methods such as contrast enhancement, noise reduction, and colour normalisation significantly increase the quality of medical images, such as histopathology scans, providing a more reliable foundation for analysis. The U-Net model, a robust deep learning algorithm for medical image segmentation, can more effectively delineate tumor boundaries when trained on this enhanced data. This synergistic approach, where improved data quality boosts U-Net’s segmentation accuracy, enables the clearer detection of cancerous tissues. This review examines how combining these methodologies provides more accurate and reliable results, ultimately contributing to improved early diagnosis, effective treatment planning, and extended life expectancy for cancer patients. Therefore, this approach could be used to revolutionize early cancer detection and manage cancer treatment more effectively.