ArticleBioMed research international2021
Lung Infection Segmentation for COVID-19 Pneumonia Based on a Cascade Convolutional Network from CT Images.
Article in BioMed research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- A 19-layer convolutional neural network for accurate COVID-19 detection in chest X-ray images: comparative analysis with pretrained networks.Scientific reports · 2026Article
- Comparing Different Data Partitioning Strategies for Segmenting Areas Affected by COVID-19 in CT Scans.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial Intelligence and Infectious Disease Imaging.The Journal of infectious diseases · 2023Article
- An Intelligent Sorting Method of Film in Cotton Combining Hyperspectral Imaging and the AlexNet-PCA Algorithm.Sensors (Basel, Switzerland) · 2023Article
- Remora Namib Beetle Optimization Enabled Deep Learning for Severity of COVID-19 Lung Infection Identification and Classification Using CT Images.Sensors (Basel, Switzerland) · 2023Article
- Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment.Sensors (Basel, Switzerland) · 2023Review
- Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review.SN computer science · 2023Article
- COVID-19 prediction using Caviar Squirrel Jellyfish Search Optimization technique in fog-cloud based architecture.PloS one · 2023Article
- A novel deep neural network model based Xception and genetic algorithm for detection of COVID-19 from X-ray images.Annals of operations research · 2022Article
- Active deep learning from a noisy teacher for semi-supervised 3D image segmentation: Application to COVID-19 pneumonia infection in CT.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2022Article
- Comprehensive Survey of Machine Learning Systems for COVID-19 Detection.Journal of imaging · 2022Review
- COVLIAS 1.0Diagnostics (Basel, Switzerland) · 2022Article
- Investigation of Effectiveness of Shuffled Frog-Leaping Optimizer in Training a Convolution Neural Network.Journal of healthcare engineering · 2022Article
- COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts.Diagnostics (Basel, Switzerland) · 2021Article
- Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography.Diagnostics (Basel, Switzerland) · 2021Article
- COVLIAS 1.0: Lung Segmentation in COVID-19 Computed Tomography Scans Using Hybrid Deep Learning Artificial Intelligence Models.Diagnostics (Basel, Switzerland) · 2021Article
- A Novel Image Processing Approach to Enhancement and Compression of X-ray Images.International journal of environmental research and public health · 2021Article
- Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images.Scientific reports · 2021Article
- Osteolysis: A Literature Review of Basic Science and Potential Computer-Based Image Processing Detection Methods.Computational intelligence and neuroscience · 2021Review
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The COVID-19 pandemic is a global, national, and local public health concern which has caused a significant outbreak in all countries and regions for both males and females around the world. Automated detection of lung infections and their boundaries from medical images offers a great potential to augment the patient treatment healthcare strategies for tackling COVID-19 and its impacts. Detecting this disease from lung CT scan images is perhaps one of the fastest ways to diagnose patients. However, finding the presence of infected tissues and segment them from CT slices faces numerous challenges, including similar adjacent tissues, vague boundary, and erratic infections. To eliminate these obstacles, we propose a two-route convolutional neural network (CNN) by extracting global and local features for detecting and classifying COVID-19 infection from CT images. Each pixel from the image is classified into the normal and infected tissues. For improving the classification accuracy, we used two different strategies including fuzzy
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