3D RegNet: Deep Learning Model for COVID-19 Diagnosis on Chest CT Image
This work addresses the need for faster and more accurate COVID-19 diagnosis for medical practitioners, but it is incremental as it applies an existing 3D deep learning approach to a specific medical dataset.
The paper tackles the problem of diagnosing COVID-19 from chest CT images by proposing a 3D-RegNet-based neural network to address issues like time consumption and low accuracy in traditional methods, achieving an f1 score of 0.8379 and AUC of 0.8807 on a test set.
In this paper, a 3D-RegNet-based neural network is proposed for diagnosing the physical condition of patients with coronavirus (Covid-19) infection. In the application of clinical medicine, lung CT images are utilized by practitioners to determine whether a patient is infected with coronavirus. However, there are some laybacks can be considered regarding to this diagnostic method, such as time consuming and low accuracy. As a relatively large organ of human body, important spatial features would be lost if the lungs were diagnosed utilizing two dimensional slice image. Therefore, in this paper, a deep learning model with 3D image was designed. The 3D image as input data was comprised of two-dimensional pulmonary image sequence and from which relevant coronavirus infection 3D features were extracted and classified. The results show that the test set of the 3D model, the result: f1 score of 0.8379 and AUC value of 0.8807 have been achieved.