ArticleInternational journal of imaging systems and technology2021
Classification of Coronavirus (COVID-19) from X-ray and CT images using shrunken features.
Article in International journal of imaging systems and technology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 4 of them syntheses that pooled it.
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Who cites it
52 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review.Computer methods and programs in biomedicine · 2022Pooled it
- A Survey on Machine Learning and Internet of Medical Things-Based Approaches for Handling COVID-19: Meta-Analysis.Frontiers in public health · 2022Pooled it
- Medical image processing and COVID-19: A literature review and bibliometric analysis.Journal of infection and public health · 2022Pooled it
- X-Ray Equipped with Artificial Intelligence: Changing the COVID-19 Diagnostic Paradigm during the Pandemic.BioMed research international · 2021Pooled it
- Detection of Confounders and Potential Confounders in Computed Tomography Lung Datasets.Journal of imaging informatics in medicine · 2026Article
- A Flexible Multi-Channel Deep Network Leveraging Texture and Spatial Features for Diagnosing New COVID-19 Variants in Lung CT Scans.Tomography (Ann Arbor, Mich.) · 2025Article
- An effective flowchart for multimodal brain tumor binary classification with ranked 3D texture features.Scientific reports · 2025Article
- COVID-19 infection segmentation using hybrid deep learning and image processing techniques.Scientific reports · 2023Article
- Multi-class deep learning architecture for classifying lung diseases from chest X-Ray and CT images.Scientific reports · 2023Article
- COVID-19 cluster identification and support vector machine classifier model construction using global healthcare and socio-economic features.Epidemiology and infection · 2023Article
- Deep learning for deterioration prediction of COVID-19 patients based on time-series of three vital signs.Scientific reports · 2023Article
- Towards an ML-based semantic IoT for pandemic management: A survey of enabling technologies for COVID-19.Neurocomputing · 2023Article
- Prediction models for respiratory outcomes in patients with COVID-19: integration of quantitative computed tomography parameters, demographics, and laboratory features.Journal of thoracic disease · 2023Article
- A hybrid deep learning approach for COVID-19 detection based on genomic image processing techniques.Scientific reports · 2023Article
- Automated diagnosis of COVID-19 using radiological modalities and Artificial Intelligence functionalities: A retrospective study based on chest HRCT database.Biomedical signal processing and control · 2023Article
- Stacked ensemble deep learning for pancreas cancer classification using extreme gradient boosting.Frontiers in artificial intelligence · 2023Article
- Multi-modal fusion of deep transfer learning based COVID-19 diagnosis and classification using chest x-ray images.Multimedia tools and applications · 2023Article
- A robust approach for endotracheal tube localization in chest radiographs.Frontiers in artificial intelligence · 2023Article
- Classification and localization of maize leaf spot disease based on weakly supervised learning.Frontiers in plant science · 2023Article
- A new model for classification of medical CT images using CNN: a COVID-19 case study.Multimedia tools and applications · 2022Article
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3 authors.
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Abstract
Necessary screenings must be performed to control the spread of the COVID-19 in daily life and to make a preliminary diagnosis of suspicious cases. The long duration of pathological laboratory tests and the suspicious test results led the researchers to focus on different fields. Fast and accurate diagnoses are essential for effective interventions for COVID-19. The information obtained by using X-ray and Computed Tomography (CT) images is vital in making clinical diagnoses. Therefore it is aimed to develop a machine learning method for the detection of viral epidemics by analyzing X-ray and CT images. In this study, images belonging to six situations, including coronavirus images, are classified using a two-stage data enhancement approach. Since the number of images in the dataset is deficient and unbalanced, a shallow image augmentation approach was used in the first phase. It is more convenient to analyze these images with hand-crafted feature extraction methods because the dataset newly created is still insufficient to train a deep architecture. Therefore, the Synthetic minority over-sampling technique algorithm is the second data enhancement step of this study. Finally, the feature vector is reduced in size by using a stacked auto-encoder and principal component analysis methods to remove interconnected features in the feature vector. According to the obtained results, it is seen that the proposed method has leveraging performance, especially to make the diagnosis of COVID-19 in a short time and effectively. Also, it is thought to be a source of inspiration for future studies for deficient and unbalanced datasets.
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