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RISS 인기검색어
- 검색키워드
- 저자 : Thanh-Nghi Do (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
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SVM on Top of Deep Networks for Covid-19 Detection from Chest X-ray Images
Thanh-Nghi Do,Van-Thanh Le,Thi-Huong Doan 한국정보통신학회 2022 Journal of information and communication convergen Vol.20 No.3
In this study, we propose training a support vector machine (SVM) model on top of deep networks for detecting Covid-19 from chest X-ray images. We started by gathering a real chest X-ray image dataset, including positive Covid-19, normal cases, and other lung diseases not caused by Covid-19. Instead of training deep networks from scratch, we fine-tuned recent pre-trained deep network models, such as DenseNet121, MobileNet v2, Inception v3, Xception, ResNet50, VGG16, and VGG19, to classify chest X-ray images into one of three classes (Covid-19, normal, and other lung). We propose training an SVM model on top of deep networks to perform a nonlinear combination of deep network outputs, improving classification over any single deep network. The empirical test results on the real chest X-ray image dataset show that deep network models, with an exception of ResNet50 with 82.44%, provide an accuracy of at least 92% on the test set. The proposed SVM on top of the deep network achieved the highest accuracy of 96.16%.
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Tri-Thuc Vo,Thanh-Nghi Do 한국정보통신학회 2024 Journal of information and communication convergen Vol.22 No.2
In this study, we present a novel approach for enhancing chest X-ray image classification (normal, Covid-19, edema, massnodules, and pneumothorax) by combining contrastive learning and machine learning algorithms. A vast amount of unlabeleddata was leveraged to learn representations so that data efficiency is improved as a means of addressing the limited availabilityof labeled data in X-ray images. Our approach involves training classification algorithms using the extracted features from alinear fine-tuned Momentum Contrast (MoCo) model. The MoCo architecture with a Resnet34, Resnet50, or Resnet101backbone is trained to learn features from unlabeled data. Instead of only fine-tuning the linear classifier layer on the MoCopretrainedmodel, we propose training nonlinear classifiers as substitutes for softmax in deep networks. The empirical resultsshow that while the linear fine-tuned ImageNet-pretrained models achieved the highest accuracy of only 82.9% and the linearfine-tuned MoCo-pretrained models an increased highest accuracy of 84.8%, our proposed method offered a significantimprovement and achieved the highest accuracy of 87.9%.
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Huynh, Phuoc-Hai,Nguyen, Van Hoa,Do, Thanh-Nghi The Korea Institute of Information and Commucation 2019 Journal of information and communication convergen Vol.17 No.1
Currently, microarray gene expression data take advantage of the sufficient classification of cancers, which addresses the problems relating to cancer causes and treatment regimens. However, the sample size of gene expression data is often restricted, because the price of microarray technology on studies in humans is high. We propose enhancing the gene expression classification of support vector machines with generative adversarial networks (GAN-SVMs). A GAN that generates new data from original training datasets was implemented. The GAN was used in conjunction with nonlinear SVMs that efficiently classify gene expression data. Numerical test results on 20 low-sample-size and very high-dimensional microarray gene expression datasets from the Kent Ridge Biomedical and Array Expression repositories indicate that the model is more accurate than state-of-the-art classifying models.
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