  Medical image classification and segmentation based on deep learning (DL) are
emergency research topics for diagnosing variant viruses of the current
COVID-19 situation. In COVID-19 computed tomography (CT) images of the lungs,
ground glass turbidity is the most common finding that requires specialist
diagnosis. Based on this situation, some researchers propose the relevant DL
models which can replace professional diagnostic specialists in clinics when
lacking expertise. However, although DL methods have a stunning performance in
medical image processing, the limited datasets can be a challenge in developing
the accuracy of diagnosis at the human level. In addition, deep learning
algorithms face the challenge of classifying and segmenting medical images in
three or even multiple dimensions and maintaining high accuracy rates.
Consequently, with a guaranteed high level of accuracy, our model can classify
the patients' CT images into three types: Normal, Pneumonia and COVID.
Subsequently, two datasets are used for segmentation, one of the datasets even
has only a limited amount of data (20 cases). Our system combined the
classification model and the segmentation model together, a fully integrated
diagnostic model was built on the basis of ResNet50 and 3D U-Net algorithm. By
feeding with different datasets, the COVID image segmentation of the infected
area will be carried out according to classification results. Our model
achieves 94.52% accuracy in the classification of lung lesions by 3 types:
COVID, Pneumonia and Normal. For future medical use, embedding the model into
the medical facilities might be an efficient way of assisting or substituting
doctors with diagnoses, therefore, a broader range of the problem of variant
viruses in the COVID-19 situation may also be successfully solved.
