Detection of pulmonary ground-glass opacity based on deep learning computer artificial intelligence

Ye, Wenjing; Gu, Wen; Guo, Xuejun; Yi, Ping; Meng, Yishuang; Han, Fengfeng; Yu, Lei; Chen, Yi; Zhang, Guorui; Wang, Xueting

doi:10.1186/s12938-019-0627-4

Cited by 30 publications

(27 citation statements)

References 23 publications

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“…As the wide application of the arti cial intelligence technology for detection of pulmonary nodules has demonstrated great success [16,17] , computer-aided detection and analysis makes quanti cation and classi cation of COVID-19 possible. The arti cial intelligence evaluation system of COVID-19 was rapidly developed and applied to solve the insu cient expertise of radiologists and speed up screening potential new cases of COVID-19 in Wuhan city, China [18] .…”

Section: The Quantitative Analysis Of Correlation Between Pulmonary Amentioning

confidence: 99%

“…The arti cial intelligence evaluation system of COVID-19 was rapidly developed and applied to solve the insu cient expertise of radiologists and speed up screening potential new cases of COVID-19 in Wuhan city, China [18] . However, the current system has been con ned to detect pulmonary nodules [16,17] , its application in assessing the severity of COVID-19 has yet to be developed.…”

Section: The Quantitative Analysis Of Correlation Between Pulmonary Amentioning

confidence: 99%

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Development and validation of a radiomics nomogram for identification of severity of patients with COVID-19

Wang

Cheng³

et al. 2020

Preprint

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Background The coronavirus disease 2019 (COVID-19) is a pandemic now, and the severe COVID-19 determines the management and treatment, even prognosis. Thus, we aim to develop and validate a radiomics nomogram for identifying severe patients with COVID-19.Methods There were 156 and 104 patients with COVID-19 enrolled in primary and validation cohorts respectively. Radiomics features were extracted from chest CT images. Least absolute shrinkage and selection operator (LASSO) method was used for feature selection and radiomics signature building. Multivariable logistic regression analysis was used to develop a predictive model, and the radiomics signature, abnormal WBC counts, and comorbidity were incorporated and presented as a radiomics nomogram. The performance of the nomogram was assessed through its calibration, discrimination, and clinical usefulness.Results The radiomics signature consisting of 4 selected features was significantly associated with clinical condition of patients with COVID-19 in the primary and validation cohorts (P < 0.001). The radiomics nomogram including radiomics signature, comorbidity and abnormal WBC counts, showed good discrimination of severe COVID-19, with an AUC of 0.972, and good calibration in the primary cohort. Application of the nomogram in the validation cohort still gave good discrimination with an AUC of 0.978 and good calibration. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful to identify the severe COVID-19.Conclusions We present an easy-to-use radiomics nomogram to identify the severe patients with COVID-19 for better guiding a prompt management and treatment.

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Section: The Quantitative Analysis Of Correlation Between Pulmonary Amentioning

confidence: 99%

Section: The Quantitative Analysis Of Correlation Between Pulmonary Amentioning

confidence: 99%

Development and validation of a radiomics nomogram for identification of severity of patients with COVID-19

Wang

Cheng³

et al. 2020

Preprint

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“…The output of the last fully-connected layer consisted of 4096 dimensional features [16]. AlexNet had shown very useful in classification of medical imaging for diseases such as lung diseases [17], heart conditions [18,19] as well as cancer [20].…”

Section: Alexnetmentioning

confidence: 99%

Chest X-Ray Image Classification on Common Thorax Diseases using GLCM and AlexNet Deep Features

Akmal¹,

Than²,

Abdullah³

et al. 2019

IJIE

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Image processing in medical has been progressing far than it ever did when it's one of the main techniques used in the biomedical imaging system and computer aided diagnosis systems. Few of the well-known medical imaging modalities are Magnetic Resonance Imaging (MRI), Computed Tomography (CT) Scan, X-Ray and Ultrasound. The output from these imaging modalities would later be reviewed by expert for an accurate result. Computer aided diagnosis not only save time as it can process thousand images just in a few minutes, it can also be used as tool to aid the expert as a second opinion, which in turn could save more lives. Thorax consist of several organs which include heart, lungs, thymus gland, muscles and other various internal structures such as diaphragm. Thorax disease refer to diseases that affect the chest and the most common symptom is the chest pain. Some of the common thoracic disease are atelectasis, pneumothorax, pneumonia, nodule, mass, infiltration, effusion and cardiomegaly. Figure 1 shows the thorax diseases observed in chest X-ray [1]. Correct diagnosis and disease treatment are important especially during the early stage, which is the most difficult part of the process due to similarity in appearance.

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“…Different CNN architectures were investigated and modified versions were proposed with optimization methods to improve model's classification accuracy. ResNet reached the highest detection accuracy of 95% to identify "ground glass opacities" [7]. Meanwhile, DenseNet combined with a machine learning classifier achieved the classification accuracy of 99% [8] , and EfficientNet was the best among 15 other CNN classifiers based on the accuracy of 82% [9].…”

Section: Introductionmentioning

confidence: 99%

DIAG a Diagnostic Web Application Based on Lung CT Scan Images and Deep Learning

Bouzid

Yahiaoui

Lounis

et al. 2021

Studies in Health Technology and Informatics

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Coronavirus disease is a pandemic that has infected millions of people around the world. Lung CT-scans are effective diagnostic tools, but radiologists can quickly become overwhelmed by the flow of infected patients. Therefore, automated image interpretation needs to be achieved. Deep learning (DL) can support critical medical tasks including diagnostics, and DL algorithms have successfully been applied to the classification and detection of many diseases. This work aims to use deep learning methods that can classify patients between Covid-19 positive and healthy patient. We collected 4 available datasets, and tested our convolutional neural networks (CNNs) on different distributions to investigate the generalizability of our models. In order to clearly explain the predictions, Grad-CAM and Fast-CAM visualization methods were used. Our approach reaches more than 92% accuracy on 2 different distributions. In addition, we propose a computer aided diagnosis web application for Covid-19 diagnosis. The results suggest that our proposed deep learning tool can be integrated to the Covid-19 detection process and be useful for a rapid patient management.

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Detection of pulmonary ground-glass opacity based on deep learning computer artificial intelligence

Cited by 30 publications

References 23 publications

Development and validation of a radiomics nomogram for identification of severity of patients with COVID-19

Development and validation of a radiomics nomogram for identification of severity of patients with COVID-19

Chest X-Ray Image Classification on Common Thorax Diseases using GLCM and AlexNet Deep Features

DIAG a Diagnostic Web Application Based on Lung CT Scan Images and Deep Learning

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