A Fully Automated Deep Learning-based Network For Detecting COVID-19 from a New And Large Lung CT Scan Dataset

Abstract

<jats:p>COVID-19 is a severe global problem, and one of the primary ways to decrease its casualties is the infected person's identification at the proper time. AI can play a significant role in these cases by monitoring and detecting infected persons in early-stage. In this paper, we aim to propose a high- speed and accurate fully-automated method to detect COVID-19 from the patient's CT scan. We introduce a new dataset that contains 48260 CT scan images from 282 normal persons and 15589 images from 95 patients with COVID-19 infection. Our proposed automated system takes all the CT scan image sequences of a patient as the input and determines if the patient is infected with COVID-19. At the first stage, this system runs the proposed image processing algorithm to discard those CT images that inside the lung is not properly visible in them. This helps to reduce the number of images that shall be processed, so it reduces the processing time. Also, running this algorithm makes the deep network at the next stage to analyze only the proper images and thus reduces false detections. At the next stage, we propose a new modified deep convolutional network that is based on ResNet50V2 and is enhanced by the feature pyramid network for classifying the selected CT images into COVID-19 or normal. After running these two phases, if enough number of chosen CT scan images of a patient be identified as COVID-19, the system considers that patient, infected to this disease. In the single image classification stage, the ResNet50V2 with feature pyramid network achieved 98.49% accuracy on more than 7996 validation images. At the fully automated phase, the automated system correctly identified almost 237 patients from 245 patients on average between five-folds with high speed. In the end, we also investigate the classified images with a feature visualization algorithm to indicate the area of infections in each image. We are implementing these materials on some medical centers in Iran, and we hope that it would be a great help in Intelligence disease detection anywhere.</jats:p>

Applications of artificial intelligence in battling against covid-19: A literature review

“…Other examples of deep neural networks in processing CT scan images of chest include [200] , [201] , [202] , [203] , [204] , [205] , [206] , [207] , [208] , [209] , [210] , [211] , [212] , [213] , [214] , [215] , [216] , [217] , [218] , [219] , [220] , [221] , [222] , [223] , [224] , [225] , [226] , [227] , [228] , [229] , [230] , [231] , [232] , [233] , [234] , [235] .…”

Adversarial Graph Learning and Deep Learning Techniques for improving diagnosis within CT and Ultrasound images

“…He et al [22] propose using contrastive self-supervised learning combined with transfer learning for doing domain adaptation and for obtaining more meaningful feature representations. Rahimzadeh et al [13] propose a modified version of ResNet50V2 [28] enhanced with a feature pyramid network for classifying CT scans previously selected by a hand-made selection algorithm. Polsinelli et al [14] propose a lightweight CNN architecture based on SqueezeNet [29] for discriminating between positive and negative COVID-19 images.…”

“…Rahimzadeh et at. [13] built a dataset containing 48260 CT scan images from 282 non-infected persons, and 15589 images from 95 patients infected with COVID-19. The Italian Society of Medical and Interventional Radiology (SIRM) [34] makes available medical images (both X-rays and CTs) for 68 patients infected with COVID-19.…”

“…The well-trained radiologists are highly occupied during the outbreak of COVID-19, and they are unable to investigate all the received CT scans in a timely fashion. Motivated by these challenges and by the urgent need for alternatives, several artificial intelligence-based solutions for automatic diagnosis of COVID-19 from CT scans and chest radiographic data have been proposed [3], [8][9][10][11][12][13][14].…”

“…Traditional [17][18][19] and deep learning methods [20] were widely applied, in order to recognize malignant lesions and tumors from medical images. During the last months, many approaches appeared, aiming to detect the COVID-19 disease from medical images, based on deep learning techniques [12][13], [21][22]. However, the accuracy of detecting HCC within ultrasound images can be further increased.…”