Abstract
Precise and fast diagnosis of COVID-19 cases play a vital role in early stage of medical treatment and prevention. Automatic detection of COVID-19 cases using the chest X-ray images and chest CT-scan images will be helpful to reduce the impact of this pandemic on the human society. We have developed a novel FractalCovNet architecture using Fractal blocks and U-Net for segmentation of chest CT-scan images to localize the lesion region. The same FractalCovNet architecture is also used for classification of chest X-ray images using transfer learning. We have compared the segmentation results using various model such as U-Net, DenseUNet, Segnet, ResnetUNet, and FCN. We have also compared the classification results with various models like ResNet5-, Xception, InceptionResNetV2, VGG-16 and DenseNet architectures. The proposed FractalCovNet model is able to predict the COVID-19 lesion with high F-measure and precision values compared to the other state-of-the-art methods. Thus the proposed model can accurately predict the COVID-19 cases and discover lesion regions in chest CT without the manual annotations of lesions for every suspected individual. An easily-trained and high-performance deep learning model provides a fast way to identify COVID-19 patients, which is beneficial to control the outbreak of SARS-II-COV.
【저자키워드】 COVID-19, U-NET, FractalCovNet, Chest X-ray classification, CT-scan image segmentation, 【초록키워드】 Treatment, pandemic, deep learning, Region, Chest CT, outbreak, early stage, predict, COVID-19 patients, lesion, Chest X-ray image, Precision, COVID-19 case, transfer, annotation, chest CT-scan, block, Fractal, identify, provide, reduce, Automatic, diagnosis of COVID-19, 【제목키워드】 classification, X-ray, Segmentation, CT-scan,