ai-corona: Radiologist-Assistant Deep Learning Framework for COVID-19 Diagnosis in Chest CT Scans

Abstract

Background: With the global outbreak of COVID-19 epidemic since early 2020, there has been considerable attention on CT-based diagnosis as an effective and reliable method. Recently, the advent of deep learning in medical diagnosis has been well proven. Convolutional Neural Networks (CNN) can be used to detect the COVID-19 infection imaging features in a chest CT scan. We introduce ai-corona, a radiologist-assistant deep learning framework for COVID-19 infection diagnosis using the chest CT scans. Method: Our dataset comprises 2121 cases of axial spiral chest CT scans in three classes; COVID-19 abnormal, non COVID-19 abnormal, and normal, from which 1764 cases were used for training and 357 cases for validation. The training set was annotated using the reports of two experienced radiologists. The COVID-19 abnormal class validation set was annotated using the general consensus of a collective of criteria that indicate COVID-19 infection. Moreover, the validation sets for the non COVID-19 abnormal and the normal classes were annotated by a different experienced radiologist. ai-corona constitutes a CNN-based feature extractor conjoined with an average pooling and a fully-connected layer to classify a given chest CT scan into the three aforementioned classes. Results: We compare the diagnosis performance of ai-corona, radiologists, and model-assisted radiologists for six combinations of distinguishing between the three mentioned classes, including COVID-19 abnormal vs. others, COVID-19 abnormal vs. normal, COVID-19 abnormal vs. non COVID-19 abnormal, non COVID-19 abnormal vs. others, normal vs. others, and normal vs. abnormal. ai-corona achieves an AUC score of 0.989 (95% CI: 0.984, 0.994), 0.997 (95% CI: 0.995, 0.999), 0.986 (95% CI: 0.981, 0.991), 0.959 (95% CI: 0.944, 0.974), 0.978 (95% CI: 0.968, 0.988), and 0.961 (95% CI: 0.951, 0.971) in each combination, respectively. By employing Bayesian statistics to calculate the accuracies at a 95% confidence interval, ai-corona surpasses the radiologists in distinguishing between the COVID-19 abnormal class and the other two classes (especially the non COVID-19 abnormal class). Our results show that radiologists' diagnosis performance improves when incorporating ai-corona's prediction. In addition, we also show that RT-PCR's diagnosis has a much lower sensitivity compared to all the other methods.

A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)

“…The slices with the most lung regions are selected while the others are discarded. In [61] , the middle 50% slices from 3D CT scans are selected. The individual slices or features extracted from these slices are directly used for optimizing the pre-trained models.…”

“…Afterward, for the proper diagnosis of COVID-19, Yousefzadeh et al [61] introduced a deep learning framework called ai-corona which is worked based on CT images. The system is comprised of several variants of CNN named DenseNet, ResNet, Xception, and EfficientNetB0.…”

Improving effectiveness of different deep learning-based models for detecting COVID-19 from computed tomography (CT) images

“…Confusion values and performance results for each model are given in Tables 6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39, 40 and 41.…”

“…?ResNet50 model as the best model with 97:4% accuracy, 92:2% sensitivity and 99:1% AUC values. Yousefzadeh et al [17] introduced a deep learning framework using different CNN architectures DenseNet, ResNet, Xception and EfficientNetB0. The datasets contain a total of 2124 CT images.…”

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

“…In [236] , a combination of Nu-SVM, DenseNet and ResNet DNNs are used to process CT scan images. A CNN-based feature extractor algorithm conjoined with an average pooling and a classifier is used in [237] to process CT scan images. A combination of white balance followed by Contrast Limited Adaptive histogram Equalization and depth-wise separable CNN is proposed in [238] .…”