DeepChestNet: Artificial intelligence approach for COVID‐19 detection on computed tomography images

Abstract

AbstractThe conventional approach for identifying ground glass opacities (GGO) in medical imaging is to use a convolutional neural network (CNN), a subset of artificial intelligence, which provides promising performance in COVID‐19 detection. However, CNN is still limited in capturing structured relationships of GGO as the texture and shape of the GGO can be confused with other structures in the image. In this paper, a novel framework called DeepChestNet is proposed that leverages structured relationships by jointly performing segmentation and classification on the lung, pulmonary lobe, and GGO, leading to enhanced detection of COVID‐19 with findings. The performance of DeepChestNet in terms of dice similarity coefficient is 99.35%, 99.73%, and 97.89% for the lung, pulmonary lobe, and GGO segmentation, respectively. The experimental investigations on DeepChestNet‐Lung, DeepChestNet‐Lobe and DeepChestNet‐COVID datasets, and comparison with several state‐of‐the‐art approaches reveal the great potential of DeepChestNet for diagnosis of COVID‐19 disease.