A fully automated deep learning-based network for detecting COVID-19 from a new and large lung CT scan dataset. (July 2021) - Rahimzadeh, Mohammad; Attar, Abolfazl; Sakhaei, Seyed Mohammad
Graphical abstract: Highlights: We introduce and share a new and large dataset of original CT scans. We introduce a fully automated system for detecting COVID-19 cases that acts with high accuracy and speed. We propose a new architecture to improve the classification accuracy of images containing important objects in various scales (especially in small scales), which has shown very good improvement. We evaluated our model in two ways: one based on single-image classification (tested on more than 7, 996 images) and the other one for evaluating the automated diagnosis system (tested on 235 patients and 41, 892 images). We have segmented the infection areas of the CT scan images. Abstract: This paper aims to propose a high-speed and accurate fully-automated method to detect COVID-19 from the patient's chest CT scan images. We introduce a new dataset that contains 48, 260 CT scan images from 282 normal persons and 15, 589 images from 95 patients with COVID-19 infections. At the first stage, this system runs our proposed image processing algorithm that analyzes the view of the lung to discard those CT images that inside the lung is not properly visible in them. This action helps to reduce the processing time and false detections. At the next stage, we introduce a novel architecture for improving the classification accuracy of convolutional networks on images containing small important objects. Our architecture applies a new feature pyramid network designed for classification problems to the ResNet50V2 model so the model becomes able to investigate different resolutions of the image and do not lose the data of small objects. As the infections of COVID-19 exist in various scales, especially many of them are tiny, using our method helps to increase the classification performance remarkably. After running these two phases, the system determines the condition of the patient using a selected threshold. We are the first to evaluate our system in two different ways on Xception, ResNet50V2, and our model. In the single image classification stage, our model achieved 98.49% accuracy on more than 7996 test images. At the patient condition identification phase, the system correctly identified almost 234 of 245 patients with high speed. Our dataset is accessible at https://github.com/mr7495/COVID-CTset .