Global Adaptive Histogram Feature Network for Automatic Segmentation of Infection Regions in CT Images

Abstract

Abstract We applied deep learning techniques in lung CT image diagnosis of COVID-19 for accurate segmentation of disease diagnosis. We propose a new deep learning framework, GAHFNet, specifically designed for automatic segmentation of COVID-19 lung CT images. GAHFNet outperforms other traditional and the state-of-the-art methods in various evaluation metrics, demonstrating the effectiveness and the efficiency of the proposed method in this task. This article discusses the limitations of current diagnostic methods, such as RT-PCR, and highlights the advantages of deep learning, including its ability to automatically learn features and handle complex lesion morphology and texture. Furthermore, the proposed method addresses the challenges in lung CT image segmentation, such as the complex image structure and difficulties of distinguishing COVID-19 pneumonia lesions from other pathologies. We provide the detailed description of the proposed GAHFNet. Finally, comprehensive experiments are carried out to evaluate the performance of GAHFNet, demonstrating that GAHFNet is able to facilitate the application of artificial intelligence in COVID-19 diagnosis and achieve accurate automatic segmentation of infected areas in COVID-19 lung CT images.