Multi-task Deep Learning Based CT Imaging Analysis For COVID-19: Classification and Segmentation

Amine Amyar; Romain Modzelewski; Su Ruan

For the detection of COVID-19 and the segmentation of the infection at the lung level, several deep learning works on xchest ray images and CT scans have emerged and reported in [12] . In [13] Ali Narin et al. created deep convolutional neural networks to automatically detect COVID-19 on X-ray images. To that end, they used transfer learning based approach with a very deep architectures such as ResNet50, InceptionV3 and Inception-ResNetV2. The algorithms were trained on the basis of 100 images (50 COVID vs 50 non-COVID) in 5 cross-validation. Authors claimed 97 % of accuracy using In-ceptionV3 and 87% using Inception-ResNetV2, however, due to the very limited size of patient and the very deep models, overfiting would rise and could not be ruled-out, hence the need to validate those results in a larger database is necessary. Also in [14] , Hemdan et al. created several deep learning models to classify x-ray images into COVID vs non-COVID classes reporting best results with an accuracy of 90% using VGG16. Again, the database was very limited with only 50 cases (25 COVID vs 25 non-COVID). A resembling study was conducted by wang and wang [15] where they trained a CNN on the ImageNET database [16] then fine-tuned on x-ray images to classify cases into one of four classes: normal, bacterial, non-COVID-19 viral and COVID-19 viral infection, with an overall performance of 83.5%. For CT images, Jinyu Zhao et al [17] created a container for CT scans initially with 275 CT COVID-19 on which they also applied a transfer learning algorithm using chest-x-ray14 [18] with 169-layer DenseNet [19] . The performance of the model is 84.7% with an area under the ROC curve of 82.4%. As of today, the database contains 347 CT images for COVID-19 patients and 397 for non-COVID patients.