Classification of COVID-19 Chest CT Images Based on Ensemble Deep Learning

Li, Xiaoshuo; Tan, Wenjun; Liu, Pan; Zhou, Qinghua; Yang, Jinzhu

doi:10.1155/2021/5528441

Cited by 46 publications

(25 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Vgg16_bn and vgg19_bn, CNNs with fewer layers, can produce better effects [ 13 , 14 ]. ResNet18 and ResNet50, CNNs with more layers, do not have such favorable effects [ 15 ].…”

Section: Discussionmentioning

confidence: 99%

Prediction of Hearing Prognosis of Large Vestibular Aqueduct Syndrome Based on the PyTorch Deep Learning Model

Duan

Pan

et al. 2022

Journal of Healthcare Engineering

View full text Add to dashboard Cite

In order to compare magnetic resonance imaging (MRI) findings of patients with large vestibular aqueduct syndrome (LVAS) in the stable hearing loss (HL) group and the fluctuating HL group, this paper provides reference for clinicians' early intervention. From January 2001 to January 2016, patients with hearing impairment diagnosed as LVAS in infancy in the Department of Otorhinolaryngology, Head and Neck Surgery, Children's Hospital of Fudan University were collected and divided into the stable HL group (n = 29) and the fluctuating HL group (n = 30). MRI images at initial diagnosis were collected, and various deep learning neural network training models were established based on PyTorch to classify and predict the two series. Vgg16_bn, vgg19_bn, and ResNet18, convolutional neural networks (CNNs) with fewer layers, had favorable effects for model building, with accs of 0.9, 0.8, and 0.85, respectively. ResNet50, a CNN with multiple layers and an acc of 0.54, had relatively poor effects. The GoogLeNet-trained model performed best, with an acc of 0.98. We conclude that deep learning-based radiomics can assist doctors in accurately predicting LVAS patients to classify them into either fluctuating or stable HL types and adopt differentiated treatment methods.

show abstract

“…Vgg16_bn and vgg19_bn, CNNs with fewer layers, can produce better effects [ 13 , 14 ]. ResNet18 and ResNet50, CNNs with more layers, do not have such favorable effects [ 15 ].…”

Section: Discussionmentioning

confidence: 99%

Prediction of Hearing Prognosis of Large Vestibular Aqueduct Syndrome Based on the PyTorch Deep Learning Model

Duan

Pan

et al. 2022

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…Second-order texture features [ 31 ] such as correlation, contrast, homogeneity, and energy are computed by the GLCM pattern. Twelve DWT-PCA-based texture features extracted from each of the 160 IWOA-, WOA-, SSA-, and SCA-based segmented images are fed into random forest [ 32 ] for training. Random forest is tested with DWT-PCA-based texture features of each of 40 IWOA-, WOA-, SSA-, and SCA-based segmented images.…”

Section: Experiments and Discussionmentioning

confidence: 99%

Efficient COVID-19 CT Scan Image Segmentation by Automatic Clustering Algorithm

Shivahare

Gupta²

2022

Journal of Healthcare Engineering

View full text Add to dashboard Cite

This article addresses automated segmentation and classification of COVID-19 and normal chest CT scan images. Segmentation is the preprocessing step for classification, and 12 DWT-PCA-based texture features extracted from the segmented image are utilized as input for the random forest machine-learning algorithm to classify COVID-19/non-COVID-19 disease. Diagnosing COVID-19 disease through an RT-PCR test is a time-consuming process. Sometimes, the RT-PCR test result is not accurate; that is, it has a false negative, which can cause a threat to the person’s life due to delay in starting the specified treatment. At this moment, there is an urgent need to develop a reliable automatic COVID-19 detection tool that can detect COVID-19 disease from chest CT scan images within a shorter period and can help doctors to start COVID-19 treatment at the earliest. In this article, a variant of the whale optimization algorithm named improved whale optimization algorithm (IWOA) is introduced. The efficiency of the IWOA is tested for unimodal (F1–F7), multimodal (F8–F13), and fixed-dimension multimodal (F14–F23) benchmark functions and is compared with the whale optimization algorithm (WOA), salp swarm optimization (SSA), and sine cosine algorithm (SCA). The experiment is carried out in 30 trials and population size, and iterations are set as 30 and 100 under each trial. IWOA achieves faster convergence than WOA, SSA, and SCA and enhances the exploitation and exploration phases of WOA, avoiding local entrapment. IWOA, WOA, SSA, and SCA utilized Otsu’s maximum between-class variance criteria as fitness function to compute optimal threshold values for multilevel medical CT scan image segmentation. Evaluation measures such as accuracy, specificity, precision, recall, Gmean, F_measure, SSIM, and 12 DWT-PCA-based texture features are computed. The experiment showed that the IWOA is efficient and achieved better segmentation evaluation measures and better segmentation mask in comparison with other methods. DWT-PCA-based texture features extracted from each of the 160 IWOA-, WOA-, SSA-, and SCA-based segmented images are fed into random forest for training, and random forest is tested with DWT-PCA-based texture features extracted from each of the 40 IWOA-, WOA-, SSA-, and SCA-based segmented images. Random forest has reported a promising classification accuracy of 97.49% for the DWT-PCA-based texture features, which are extracted from IWOA-based segmented images.

show abstract

“…The current classification algorithm relies heavily on the fine labeling of lesion regions, which increases the workload of data labeling and causes inconvenience to operation. Some researchers [ 22 ] put forward an integrated deep learning-based adjuvant diagnosis algorithm. Stacked generalization ensemble learning was combined with VGG16 deep learning to form cascade connection classifiers to classify and verify novel coronavirus pneumonia patients, common pneumonia patients, and normal control patients.…”

Section: Discussionmentioning

confidence: 99%

Intelligence Classification Algorithm-Based Drug-Resistant Pulmonary Tuberculosis Computed Tomography Imaging Features and Influencing Factors

Jiang

Zhao

Fan

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The drug resistance and influencing factors of patients with pulmonary tuberculosis were investigated, and a dual attention dilated residual network (DADRN) algorithm was proposed. The algorithm was applied to process and analyze lung computed tomography (CT) images of 400 included patients with pulmonary tuberculosis. Besides, sparse code book algorithm and bag of visual word (BOVW) algorithms were introduced and compared, and the influencing factors of pulmonary tuberculosis drug resistance were analyzed. The results demonstrated that the localization precision of lung consolidation, nodules, and cavities by the DADRN algorithm reached 91.2%, 92.5%, and 93.8%, respectively. The recall rate of the three algorithms amounted to 83.55%, 84.5%, and 86.4%, respectively. Both localization precision and recall rate of the DADRN algorithm were higher than those of other two algorithms ( P < 0.05 ). The drug resistance rate of streptomycin, isoniazid, and rifampin of the patients aged between 40 and 59 was all higher than those of the patients in other age groups. The drug resistance rate of streptomycin, isoniazid, and rifampin of retreated patients was all higher than those of patients initially treated. The drug resistance rate of streptomycin, isoniazid, and rifampin of the patients with tuberculosis contact was all higher than those of the patients without tuberculosis contact ( P < 0.05 ). Based on the above results, the accuracy of CT images processed by dual attention-based dilated residual classification network algorithm was higher than that processed by other two algorithms. Age, medical history, and history of exposure to tuberculosis were the influencing factors of the drug resistance of patients with pulmonary tuberculosis.

show abstract

Classification of COVID-19 Chest CT Images Based on Ensemble Deep Learning

Cited by 46 publications

References 19 publications

Prediction of Hearing Prognosis of Large Vestibular Aqueduct Syndrome Based on the PyTorch Deep Learning Model

Prediction of Hearing Prognosis of Large Vestibular Aqueduct Syndrome Based on the PyTorch Deep Learning Model

Efficient COVID-19 CT Scan Image Segmentation by Automatic Clustering Algorithm

Intelligence Classification Algorithm-Based Drug-Resistant Pulmonary Tuberculosis Computed Tomography Imaging Features and Influencing Factors

Contact Info

Product

Resources

About