Spectral analysis for pulmonary nodule detection using the optimal fractional S-Transform

Sun, Lingma; Wang, Zhuoran; Pu, Hong; Yuan, Guohui; Guo, Lu; Tian, Pu; Peng, Zhenming

doi:10.1016/j.compbiomed.2020.103675

Cited by 13 publications

(3 citation statements)

References 40 publications

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“…Changing a picture to the time recurrence area by the optimal fractional S-transform (OFrST) strategy can give important data and highlights, which can be utilized to analyze ailments. This technique was applied to lung CT pictures by [130] to identify and separate knobs from the vessels. In [131] they utilized the Teager–Kaiser vitality (TKE) in the time–recurrence area to get the vitality circulation and describe lung knobs with a sensitivity of 97.87%.…”

Section: Performance Evaluationmentioning

confidence: 99%

Notice of Retraction: AI Techniques for COVID-19

Hussain¹,

et al. 2020

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Artificial Intelligence (AI) intent is to facilitate human limits. It is getting a standpoint on human administrations, filled by the growing availability of restorative clinical data and quick progression of insightful strategies. Motivated by the need to highlight the need for employing AI in battling the COVID-19 Crisis, this survey summarizes the current state of AI applications in clinical administrations while battling COVID-19. Furthermore, we highlight the application of Big Data while understanding this virus. We also overview various intelligence techniques and methods that can be applied to various types of medical information-based pandemic. We classify the existing AI techniques in clinical data analysis, including neural systems, classical SVM, and edge significant learning. Also, an emphasis has been made on regions that utilize AI-oriented cloud computing in combating various similar viruses to COVID-19. This survey study is an attempt to benefit medical practitioners and medical researchers in overpowering their faced difficulties while handling COVID-19 big data. The investigated techniques put forth advances in medical data analysis with an exactness of up to 90%. We further end up with a detailed discussion about how AI implementation can be a huge advantage in combating various similar viruses.

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Section: Performance Evaluationmentioning

confidence: 99%

Notice of Retraction: AI Techniques for COVID-19

Hussain¹,

et al. 2020

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“…This method was applied on lung CT images by Sun et al to detect and differentiate nodules from the vessels. They employed the Teager-Kaiser energy (TKE) in the time-frequency domain to obtain the energy distribution and characterize lung nodules with 97.87% sensitivity [17]. Sun et al concluded that the CNN represented higher performance than deep belief network (DBN) and stacked denoising autoencoder (SDAE) in diagnosing malignant lung nodules with an area under the curve (AUC) of 0.899 [18].…”

Section: Introductionmentioning

confidence: 99%

Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks

Ardakani

Kanafi

Acharya

et al. 2020

Computers in Biology and Medicine

752

567

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Fast diagnostic methods can control and prevent the spread of pandemic diseases like coronavirus disease 2019 (COVID-19) and assist physicians to better manage patients in high workload conditions. Although a laboratory test is the current routine diagnostic tool, it is time-consuming, imposing a high cost and requiring a wellequipped laboratory for analysis. Computed tomography (CT) has thus far become a fast method to diagnose patients with COVID-19. However, the performance of radiologists in diagnosis of COVID-19 was moderate. Accordingly, additional investigations are needed to improve the performance in diagnosing COVID-19. In this study is suggested a rapid and valid method for COVID-19 diagnosis using an artificial intelligence technique based. 1020 CT slices from 108 patients with laboratory proven COVID-19 (the COVID-19 group) and 86 patients with other atypical and viral pneumonia diseases (the non-COVID-19 group) were included. Ten well-known convolutional neural networks were used to distinguish infection of COVID-19 from non-COVID-19 groups:

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“…Nowadays, CAD systems can help and allow radiologists to make a better decision, especially in CT lung imaging [ 20 – 22 ]. It also help to detect lung abnormalities [ 23 , 24 ] and pulmonary fibrosis [ 25 , 26 ], manage lung nodules [ 27 , 28 ], and differentiate nodules from interferential vessels [ 29 , 30 ]. In this work, we have investigated the potential of using the CAD system to diagnose and manage patients with COVID-19 pneumonia disease.…”

Section: Introductionmentioning

confidence: 99%

COVIDiag: a clinical CAD system to diagnose COVID-19 pneumonia based on CT findings

et al. 2020

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Objectives CT findings of COVID-19 look similar to other atypical and viral (non-COVID-19) pneumonia diseases. This study proposes a clinical computer-aided diagnosis (CAD) system using CT features to automatically discriminate COVID-19 from non-COVID-19 pneumonia patients. Methods Overall, 612 patients (306 COVID-19 and 306 non-COVID-19 pneumonia) were recruited. Twenty radiological features were extracted from CT images to evaluate the pattern, location, and distribution of lesions of patients in both groups. All significant CT features were fed in five classifiers namely decision tree, K-nearest neighbor, naïve Bayes, support vector machine, and ensemble to evaluate the best performing CAD system in classifying COVID-19 and non-COVID-19 cases. Results Location and distribution pattern of involvement, number of the lesion, ground-glass opacity (GGO) and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features to classify COVID-19 from non-COVID-19 groups. Our proposed CAD system obtained the sensitivity, specificity, and accuracy of 0.965, 93.54%, 90.32%, and 91.94%, respectively, using ensemble (COVIDiag) classifier. Conclusions This study proposed a COVIDiag model obtained promising results using CT radiological routine features. It can be considered an adjunct tool by the radiologists during the current COVID-19 pandemic to make an accurate diagnosis. Key Points • Location and distribution of involvement, number of lesions, GGO and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features between COVID-19 from non-COVID-19 groups. • The proposed CAD system, COVIDiag, could diagnose COVID-19 pneumonia cases with an AUC of 0.965 (sensitivity = 93.54%; specificity = 90.32%; and accuracy = 91.94%). • The AUC, sensitivity, specificity, and accuracy obtained by radiologist diagnosis are 0.879, 87.10%, 88.71%, and 87.90%, respectively.

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Spectral analysis for pulmonary nodule detection using the optimal fractional S-Transform

Cited by 13 publications

References 40 publications

Notice of Retraction: AI Techniques for COVID-19

Notice of Retraction: AI Techniques for COVID-19

Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks

COVIDiag: a clinical CAD system to diagnose COVID-19 pneumonia based on CT findings

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