ArticleScientific reports2021
Explainable DCNN based chest X-ray image analysis and classification for COVID-19 pneumonia detection.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- A comprehensive review of explainable artificial intelligence in healthcare methods, evaluation, and clinical integration.iScience · 2026Review
- Deciphering the Feature Representation of Deep Neural Networks for High-Performance AI.IEEE transactions on pattern analysis and machine intelligence · 2024Article
- Omni-dimensional dynamic convolution feature coordinate attention network for pneumonia classification.Visual computing for industry, biomedicine, and art · 2024Article
- An augmentation aided concise CNN based architecture for COVID-19 diagnosis in real time.Scientific reports · 2024Article
- Effective processing pipeline PACE 2.0 for enhancing chest x-ray contrast and diagnostic interpretability.Scientific reports · 2023Article
- Explainable Artificial Intelligence (XAI) for Deep Learning Based Medical Imaging Classification.Journal of imaging · 2023Article
- Applying machine learning methods to enable automatic customisation of knee replacement implants from CT data.Scientific reports · 2023Article
- LWSNet - a novel deep-learning architecture to segregate Covid-19 and pneumonia from x-ray imagery.Multimedia tools and applications · 2023Article
- A Survey on Medical Explainable AI (XAI): Recent Progress, Explainability Approach, Human Interaction and Scoring System.Sensors (Basel, Switzerland) · 2022Review
- CXGNet: A tri-phase chest X-ray image classification for COVID-19 diagnosis using deep CNN with enhanced grey-wolf optimizer.Biomedical signal processing and control · 2022Article
- Pre-processing methods in chest X-ray image classification.PloS one · 2022Article
- COVID-19 Classification from Chest X-Ray Images: A Framework of Deep Explainable Artificial Intelligence.Computational intelligence and neuroscience · 2022Article
- Hybrid Deep-Learning and Machine-Learning Models for Predicting COVID-19.Computational intelligence and neuroscience · 2021Article
- Assessing severity of pediatric pneumonia using multimodal transformers with multi-task learning.Digital healthArticle
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
Abstract
To speed up the discovery of COVID-19 disease mechanisms by X-ray images, this research developed a new diagnosis platform using a deep convolutional neural network (DCNN) that is able to assist radiologists with diagnosis by distinguishing COVID-19 pneumonia from non-COVID-19 pneumonia in patients based on chest X-ray classification and analysis. Such a tool can save time in interpreting chest X-rays and increase the accuracy and thereby enhance our medical capacity for the detection and diagnosis of COVID-19. The explainable method is also used in the DCNN to select instances of the X-ray dataset images to explain the behavior of training-learning models to achieve higher prediction accuracy. The average accuracy of our method is above 96%, which can replace manual reading and has the potential to be applied to large-scale rapid screening of COVID-9 for widely use cases.
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