ArticleChemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society2022
Multi-classification deep CNN model for diagnosing COVID-19 using iterative neighborhood component analysis and iterative ReliefF feature selection techniques with X-ray images.
Article in Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Vision Transformers-Based Deep Feature Generation Framework for Hydatid Cyst Classification in Computed Tomography Images.Journal of imaging informatics in medicine · 2026Article
- Integrating multiple feature assessment methods to identify key predictors of repeat suicide attempts in Taiwan.BMC psychiatry · 2025Article
- Enhanced Multi-Model Deep Learning for Rapid and Precise Diagnosis of Pulmonary Diseases Using Chest X-Ray Imaging.Diagnostics (Basel, Switzerland) · 2025Article
- Reinvestigating the performance of artificial intelligence classification algorithms on COVID-19 X-Ray and CT images.iScience · 2024Article
- A hybridized feature extraction for COVID-19 multi-class classification on computed tomography images.Heliyon · 2024Article
- Building Automation Pipeline for Diagnostic Classification of Sporadic Odontogenic Keratocysts and Non-Keratocysts Using Whole-Slide Images.Diagnostics (Basel, Switzerland) · 2023Article
- Combating the COVID-19 infodemic using Prompt-Based curriculum learning.Expert systems with applications · 2023Article
- COVID-19 diagnosis utilizing wavelet-based contrastive learning with chest CT images.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2023Article
- Review on chest pathogies detection systems using deep learning techniques.Artificial intelligence review · 2023Article
- RADIC:A tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2023Article
- Swin-textural: A novel textural features-based image classification model for COVID-19 detection on chest computed tomography.Informatics in medicine unlocked · 2023Article
- A novel method using Covid-19 dataset and machine learning algorithms FOR THE MOST ACCURATE DIAGNOSIS that can be obtained in medical diagnosis.Biomedical signal processing and control · 2022Article
- Substation equipment temperature prediction based on multivariate information fusion and deep learning network.PeerJ. Computer science · 2022Article
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4 authors.
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Abstract
Background: The acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease seriously affected worldwide health. It remains an important worldwide concern as the number of patients infected with this virus and the death rate is increasing rapidly. Early diagnosis is very important to hinder the spread of the coronavirus. Therefore, this article is intended to facilitate radiologists automatically determine COVID-19 early on X-ray images. Iterative Neighborhood Component Analysis (INCA) and Iterative ReliefF (IRF) feature selection methods are applied to increase the accuracy of the performance criteria of trained deep Convolutional Neural Networks (CNN). Materials and methods: The COVID-19 dataset consists of a total of Results: Thirteen different deep CNN experiments and evaluations are successfully performed based on 80-20% of lung X-ray images for training and testing, respectively. The highest predictive values are seen in the analysis using INCA feature selection in the VGG16 network. The means of performance criteria obtained using the accuracy, sensitivity, F-score, precision, MCC, dice, Jaccard, and specificity are
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