ArticleApplied intelligence (Dordrecht, Netherlands)2022
COVID-CT-Mask-Net: prediction of COVID-19 from CT scans using regional features.
Article in Applied intelligence (Dordrecht, Netherlands), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Artificial Intelligence in Infectious Disease Diagnostic Technologies.Diagnostics (Basel, Switzerland) · 2025Review
- A diffusion model for universal medical image enhancement.Communications medicine · 2025Article
- Efficient Retrieval of Images with Irregular Patterns Using Morphological Image Analysis: Applications to Industrial and Healthcare Datasets.Journal of imaging · 2023Article
- Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework.Diagnostics (Basel, Switzerland) · 2023Article
- EffViT-COVID: A dual-path network for COVID-19 percentage estimation.Expert systems with applications · 2023Article
- Feature selection of pre-trained shallow CNN using the QLESCA optimizer: COVID-19 detection as a case study.Applied intelligence (Dordrecht, Netherlands) · 2023Article
- Detection and classification of COVID-19 by using faster R-CNN and mask R-CNN on CT images.Neural computing & applications · 2023Article
- A new model for classification of medical CT images using CNN: a COVID-19 case study.Multimedia tools and applications · 2022Article
- A novel deep learning-based method for COVID-19 pneumonia detection from CT images.BMC medical informatics and decision making · 2022Article
- Deep viewing for the identification of Covid-19 infection status from chest X-Ray image using CNN based architecture.Intelligent systems with applications · 2022Article
- CAD systems for COVID-19 diagnosis and disease stage classification by segmentation of infected regions from CT images.BMC bioinformatics · 2022Article
- One Shot Model For The Prediction of COVID-19 And Lesions Segmentation In Chest CT Scans Through The Affinity Among Lesion Mask Features.Applied soft computing · 2022Article
- Detecting COVID-19 infection status from chest X-ray and CT scanFrontiers in genetics · 2022Article
- Interactive framework for Covid-19 detection and segmentation with feedback facility for dynamically improved accuracy and trust.PloS one · 2022Article
- Analysis of CT scan images for COVID-19 pneumonia based on a deep ensemble framework with DenseNet, Swin transformer, and RegNet.Frontiers in microbiology · 2022Article
- Deep supervised learning using self-adaptive auxiliary loss for COVID-19 diagnosis from imbalanced CT images.Neurocomputing · 2021Article
- Recognition of COVID-19 from CT Scans Using Two-Stage Deep-Learning-Based Approach: CNR-IEMN.Sensors (Basel, Switzerland) · 2021Article
- Automated diagnosis of COVID stages from lung CT images using statistical features in 2-dimensional flexible analytic wavelet transform.Biocybernetics and biomedical engineeringArticle
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1 author.
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Abstract
We present COVID-CT-Mask-Net model that predicts COVID-19 in chest CT scans. The model works in two stages: in the first stage, Mask R-CNN is trained to localize and detect two types of lesions in images. In the second stage, these detections are fused to classify the whole input image. To develop the solution for the three-class problem (COVID-19, Common Pneumonia and Control), we used the COVIDx-CT data split derived from the dataset of chest CT scans collected by China National Center for Bioinformation. We use 3000 images (about 5
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