ArticleBiomedical signal processing and control2022
Automatic detection of multiple types of pneumonia: Open dataset and a multi-scale attention network.
Article in Biomedical signal processing and control, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Capuchin Red Kite–optimized Swin Transformer-based Convolutional Block Attention Module for Early Diagnosis and Classification of PneumoniaCurrent medical imaging · 2026Article
- Development and validation of a machine learning model for predicting co-infection ofTranslational pediatrics · 2025Article
- Differential diagnosis of congenital ventricular septal defect and atrial septal defect in children using deep learning-based analysis of chest radiographs.BMC pediatrics · 2024Article
- Precision Identification of Locally Advanced Rectal Cancer in Denoised CT Scans Using EfficientNet and Voting System Algorithms.Bioengineering (Basel, Switzerland) · 2024Article
- A Deep Convolutional Neural Network for Pneumonia Detection in X-ray Images with Attention Ensemble.Diagnostics (Basel, Switzerland) · 2024Article
- A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review.BMC medical imaging · 2024Review
- Artificial intelligence for segmentation and classification of lobar, lobular, and interstitial pneumonia using case-specific CT information.Quantitative imaging in medicine and surgery · 2024Article
- Detection of Pneumonia from Chest X-ray Images Utilizing MobileNet Model.Healthcare (Basel, Switzerland) · 2023Article
- COVID-DSNet: A novel deep convolutional neural network for detection of coronavirus (SARS-CoV-2) cases from CT and Chest X-Ray images.Artificial intelligence in medicine · 2022Article
- YOLOv8 framework for COVID-19 and pneumonia detection using synthetic image augmentation.Digital healthArticle
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8 authors.
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Abstract
The quick and precise identification of COVID-19 pneumonia, non-COVID-19 viral pneumonia, bacterial pneumonia, mycoplasma pneumonia, and normal lung on chest CT images play a crucial role in timely quarantine and medical treatment. However, manual identification is subject to potential misinterpretations and time-consumption issues owing the visual similarities of pneumonia lesions. In this study, we propose a novel multi-scale attention network (MSANet) based on a bag of advanced deep learning techniques for the automatic classification of COVID-19 and multiple types of pneumonia. The proposed method can automatically pay attention to discriminative information and multi-scale features of pneumonia lesions for better classification. The experimental results show that the proposed MSANet can achieve an overall precision of 97.31%, recall of 96.18%, F1-score of 96.71%, accuracy of 97.46%, and macro-average area under the receiver operating characteristic curve (AUC) of 0.9981 to distinguish between multiple classes of pneumonia. These promising results indicate that the proposed method can significantly assist physicians and radiologists in medical diagnosis. The dataset is publicly available at https://doi.org/10.17632/rf8x3wp6ss.1.
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