ArticleNeural computing & applications2023
Detection and classification of COVID-19 by using faster R-CNN and mask R-CNN on CT images.
Article in Neural computing & applications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- ARTEMIS: An Explainable AI Framework for Multi-Class COVID-19 Diagnosis with a Newly Curated Dataset.Bioengineering (Basel, Switzerland) · 2026Article
- Efficient Cerebral Infarction Segmentation Using U-Net and U-Net3 + Models.Journal of imaging informatics in medicine · 2026Article
- Turkish Chest X-Ray Report Generation Model Using the Swin Enhanced Yield Transformer (Model-SEY) Framework.Diagnostics (Basel, Switzerland) · 2025Article
- Optimizing Defect Detection on Glossy and Curved Surfaces Using Deep Learning and Advanced Imaging Systems.Sensors (Basel, Switzerland) · 2025Article
- Using a Region-Based Convolutional Neural Network (R-CNN) for Potato Segmentation in a Sorting Process.Foods (Basel, Switzerland) · 2025Article
- A multi-scale rotated ship targets detection network for remote sensing images in complex scenarios.Scientific reports · 2025Article
- The segmentation of nanoparticles with a novel approach of HRUScientific reports · 2025Article
- Deep learning models for CT image classification: a comprehensive literature review.Quantitative imaging in medicine and surgery · 2025Review
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- The Frontiers of Smart Healthcare Systems.Healthcare (Basel, Switzerland) · 2024Review
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- A Deep Learning Approach for Arabic Manuscripts Classification.Sensors (Basel, Switzerland) · 2023Article
- Optimized Xception Learning Model and XgBoost Classifier for Detection of Multiclass Chest Disease from X-ray Images.Diagnostics (Basel, Switzerland) · 2023Article
- Article
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4 authors.
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Abstract
The coronavirus (COVID-19) pandemic has a devastating impact on people's daily lives and healthcare systems. The rapid spread of this virus should be stopped by early detection of infected patients through efficient screening. Artificial intelligence techniques are used for accurate disease detection in computed tomography (CT) images. This article aims to develop a process that can accurately diagnose COVID-19 using deep learning techniques on CT images. Using CT images collected from Yozgat Bozok University, the presented method begins with the creation of an original dataset, which includes 4000 CT images. The faster R-CNN and mask R-CNN methods are presented for this purpose in order to train and test the dataset to categorize patients with COVID-19 and pneumonia infections. In this study, the results are compared using VGG-16 for faster R-CNN model and ResNet-50 and ResNet-101 backbones for mask R-CNN. The faster R-CNN model used in the study has an accuracy rate of 93.86%, and the ROI (region of interest) classification loss is 0.061 per ROI. At the conclusion of the final training, the mask R-CNN model generates mAP (mean average precision) values for ResNet-50 and ResNet-101, respectively, of 97.72% and 95.65%. The results for five folds are obtained by applying the cross-validation to the methods used. With training, our model performs better than the industry standard baselines and can help with automated COVID-19 severity quantification in CT images.
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