ArticleOptik2021
Multi-label segmentation and detection of COVID-19 abnormalities from chest radiographs using deep learning.
Article in Optik, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed, 20 citations in OpenAlex.
- Article
- NCT-CXR: Enhancing Pulmonary Abnormality Segmentation on Chest X-Rays Using Improved Coordinate Geometric Transformations.Journal of imaging · 2025Article
- MTMC-AUR2CNet: Multi-textural multi-class attention recurrent residual convolutional neural network for COVID-19 classification using chest X-ray images.Biomedical signal processing and control · 2023Article
- Transformer-Based Deep Learning Network for Tooth Segmentation on Panoramic Radiographs.Journal of systems science and complexity · 2023Article
- PDC-Net: parallel dilated convolutional network with channel attention mechanism for pituitary adenoma segmentation.Frontiers in physiology · 2023Article
- Automatic COVID-19 Lung Infection Segmentation through Modified Unet Model.Journal of healthcare engineering · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Due to COVID-19, demand for Chest Radiographs (CXRs) have increased exponentially. Therefore, we present a novel fully automatic modified Attention U-Net (CXAU-Net) multi-class segmentation deep model that can detect common findings of COVID-19 in CXR images. The architectural design of this model includes three novelties: first, an Attention U-net model with channel and spatial attention blocks is designed that precisely localize multiple pathologies; second, dilated convolution applied improves the sensitivity of the model to foreground pixels with additional receptive fields valuation, and third a newly proposed hybrid loss function combines both area and size information for optimizing model. The proposed model achieves average accuracy, DSC, and Jaccard index scores of 0.951, 0.993, 0.984, and 0.921, 0.985, 0.973 for image-based and patch-based approaches respectively for multi-class segmentation on Chest X-ray 14 dataset. Also, average DSC and Jaccard index scores of 0.998, 0.989 are achieved for binary-class segmentation on the Japanese Society of Radiological Technology (JSRT) CXR dataset. These results illustrate that the proposed model outperformed the state-of-the-art segmentation methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.