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

Amine Amyar; Romain Modzelewski; Su Ruan

Identifying COVID-19 at an early stage through imaging would indeed allow the isolation of the patient and therefore limit the spread of the disease [2] . However, physicians are very busy fighting this disease, hence the need to create decision support tools based on artificial intelligence to not only detect but also segment the infection at the lung level in the image [1] . Artificial intelligence has seen a major and rapid growth in recent years with deep neural networks [3] as a first tool to solve different problems such as object detection [4] , speech recognition [4] , and image classification [5] . More specifically, convolutional neural networks (CNNs) [6] showed astonishing results for image processing [7] . For image segmentation, several works have shown the power and robustness of these methods [8] . CNNs architectures for medical imaging also have been used with very good results [9] , for both image classification [10] or image segmentation [11] .