Computer-Aided Detection of COVID-19 from CT Images Based on Gaussian Mixture Model and Kernel Support Vector Machines Classifier

Saygılı, Ahmet

doi:10.1007/s13369-021-06240-z

Cited by 26 publications

(10 citation statements)

References 51 publications

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“…For example, both the modified AlexNet and GoogLeNet obtain the same state-of-the-art performance with a classification accuracy of 98.39%, a sensitivity of 98.97%, a specificity of 96.67%, and Matthew correlation coefficient of 99.11%. Saygl proposed a method based on image processing and machine learning to automatically detect viruses through segmented CT images with optimal accuracy values of 98.5% in dataset 1, 86.3% in dataset 2, and 94.5% in mixed dataset [ 18 ]. The screening results of other algorithms also remarkably outperform 90% on all the metrics.…”

Section: Resultsmentioning

confidence: 99%

“…In practice, a new supplementary examination approach is demanding to improve the screening accuracy and reduce the radiation dose. While a few recent studies on automated COVID-19 screening have made great progress, they only focus on designing either chest CT-based approaches or Xray-based techniques [1,2,[4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. Both chest CT and X-ray are common medical imaging methods in clinical but have three-fold limitations in the task of automated screening of COVID-19.…”

Section: Introductionmentioning

confidence: 99%

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Automated Screening of COVID-19-Based Tongue Image on Chinese Medicine

Zhang

et al. 2022

BioMed Research International

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Objective. Artificial intelligence-powered screening systems of coronavirus disease 2019 (COVID-19) are urgently demanding since the ongoing outbreak of SARS-CoV-2 worldwide. Chest CT or X-ray is not sufficient to support the large-scale screening of COVID-19 because mildly-infected patients do not have imaging features on these images. Therefore, it is imperative to exploit supplementary medical imaging strategies. Traditional Chinese medicine has played an essential role in the fight against COVID-19. Methods. In this paper, we conduct two kinds of verification experiments based on a newly-collected multi-modality dataset, which consists of three types of modalities: tongue images, chest CT scans, and X-ray images. First, we study a binary classification experiment on tongue images to verify the discriminative ability between COVID-19 and non-COVID-19. Second, we design extensive multimodality experiments to validate whether introducing tongue image can improve the screening accuracy of COVID-19 based on chest CT or X-ray images. Results. Tongue image screening of COVID-19 showed that the accuracy (ACC), sensitivity (SEN), specificity (SPEC), and Matthew correlation coefficient (MCC) of the improved AlexNet and Googlenet both reached 98.39%, 98.97%, 96.67%, and 99.11%. The fusion of chest CT and tongue images used a tandem multimodal classifier fusion strategy to achieve optimal classification, and the results and screening accuracy of COVID-19 reached 98.98%, resulting in a significant improvement of 4.75% the highest accuracy in 375 years compared with the single-modality model. The fusion of chest x-rays and tongue images also had good classification accuracy. Conclusions. Both experimental results demonstrate that tongue image not only has an excellent discriminative ability for screening COVID-19 but also can improve the screening accuracy based on chest CT or X-rays. To the best of our knowledge, it is the first work that verifies the effectiveness of tongue image on screening COVID-19. This paper provides a new perspective and a novel solution that contributes to large-scale screening toward fast stopping the pandemic of COVID-19.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated Screening of COVID-19-Based Tongue Image on Chinese Medicine

Zhang

et al. 2022

BioMed Research International

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“…The results showed that IoT-based technology and modified techniques for taking out the background worked well together. By applying GMM Filter [22,23] to reduce unwanted small items, this research completes and continues object detection on video.…”

Section: Introductionmentioning

confidence: 85%

Vehicle Detection Using Machine Learning Model with the Gaussian Mixture Model (GMM)

Rosnelly¹,

Riza²,

Wahyuni³

et al. 2022

JOWUA

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Motion tracking apps are used for a lot of different things, like finding traffic jams and counting the number of cars going through a traffic light. The datasets come from many places on the internet, like YouTube and public dataset archives. There are about 20 videos that are tagged with the words "traffic" and "traffic camera video" and run for 10 to 30 seconds. The Gaussian Mixture Models (GMM) method is the proposed model. It separates the background from the tracked object, which is needed to do motion tracking. Then, the GMM method groups pixel data based on the background color of each pixel. After the cluster is made, the input is matched as a distribution, with the most common distribution used as the background. The analysis was done using MATLAB2019B. The results of this study show that the GMM method can adapt to the background. This is shown by the fact that testing of some of the given conditions went well.

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“…The GMM is a very popular unsupervised learning technique. This technique is popular to be used to form new synthetic data on small-sized datasets [17], [18], [19], [20]- [24]. Moreover, the GMM has been widely successfully applied in the prediction system on medical-related topics such as predicting COVID-19 cases [22], liver cancer detection [25], pancreatic cancer detection [26], medical image segmentation [27], and texture characterization of brain DTI image [28].…”

Section: B Feature Selection and Data Augmentationmentioning

confidence: 99%

Impact of Feature Selection and Data Augmentation for Pregnancy Risk Detection in Indonesia

Basuki¹,

Irfan²,

Azhar³

2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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This paper aims to develop an automatic system for pregnancy risk detection in Indonesia. The system requires a sophisticated approach to achieve the required performance as a sensitive field. Existing works are developed using small-sized datasets and limited classification features. Moreover, all features treated equally make the detection results hard to interpret which features contribute more. To address these issues, we propose to combine more complex features, data augmentation methods, and feature selection techniques. We prefer to use all 118 pregnancy indicators and 400 instances from Puskesmas as an original dataset. Next, the new datasets are used to build two data augmentation methods, i.e., GMM and CTGAN. Each data augmentation method generates 2,000 new synthetic instances. Following this, five machine learning methods combined with three feature selection approaches, i.e., RFE, Random Forest, and Chi-Square, are implemented in all datasets. Through experiments, we observed that feature selection techniques play an essential role in improving classification accuracies. While the GMM-based augmentation demonstrated performance improvement, the CTGAN-based synthetic dataset depicted low performances. The best accuracy on all experiment settings reached 95%. By using Random Forest combined with RFE on a GMM-based dataset, the highest accuracy was achieved using only five features. Another notable result is that both XGBoost and Decision Tree reached the same 95% accuracy on the GMM-based dataset on only nine features. The overall results show that appropriate data augmentation and feature selection are a matter for achieving better performance in this research.

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Computer-Aided Detection of COVID-19 from CT Images Based on Gaussian Mixture Model and Kernel Support Vector Machines Classifier

Cited by 26 publications

References 51 publications

Automated Screening of COVID-19-Based Tongue Image on Chinese Medicine

Automated Screening of COVID-19-Based Tongue Image on Chinese Medicine

Vehicle Detection Using Machine Learning Model with the Gaussian Mixture Model (GMM)

Impact of Feature Selection and Data Augmentation for Pregnancy Risk Detection in Indonesia

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