A simplified U-net model for Covid-19 CT image segmentation

Abstract

The ongoing COVID-19 pandemic has highlighted the importance of accurate and efficient medical image analysis to aid in the diagnosis and treatment of patients. In particular, the segmentation of COVID-19 medical images has become a critical task to identify regions of interest, such as the infected lung areas, and to track disease progression. Traditional image segmentation methods have been widely used in medical image analysis. However, these methods are often challenged by the complex and diverse nature of COVID-19 images, as well as the limited availability of data. In this paper, we propose a simplified version of the U-Net that eliminates redundant crop operations. This simplification reduces computational complexity and memory usage, and enables the model to learn from larger input images, resulting in better performance. We evaluate the performance of our simplified U-Net model on a public COVID-19 dataset and demonstrate that our model achieves state-of-the-art results while using fewer computational resources.