ArticleDiagnostics (Basel, Switzerland)2020
Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning.
Article in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 2 of them syntheses that pooled it.
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Who cites it
50 citing papers in PubMed, 2 syntheses or guidelines pooled it, 323 citations in OpenAlex.
- Database and AI Diagnostic Tools Improve Understanding of Lung Damage, Correlation of Pulmonary Disease and Brain Damage in COVID-19.Sensors (Basel, Switzerland) · 2022Pooled it
- Pediatric chest radiograph interpretation: how far has artificial intelligence come? A systematic literature review.Pediatric radiology · 2022Pooled it
- PD-YOLO: a high-precision detection network for fine-grained lung cancer and multi-scale pulmonary nodules in CT images.Scientific reports · 2026Article
- Nanoremediation of arsenic from contaminated water by new generation graphene-based nanomaterials: a comprehensive review.RSC advances · 2026Review
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- Classification of Chest X-Ray Images Using Modern Machine Learning Techniques by Orang3 Data Mining Tool.International journal of biomedical imaging · 2026Article
- TL-PneuNet: a transfer learning-based pneumonia classification framework.Scientific reports · 2025Article
- Pneumonia Detection from Chest X-Ray Images Using Deep Learning and Transfer Learning for Imbalanced Datasets.Journal of imaging informatics in medicine · 2025Article
- Ensemble of Deep Learning Architectures with Machine Learning for Pneumonia Classification Using Chest X-rays.Journal of imaging informatics in medicine · 2025Article
- The Effect of Simulated Dose Reduction on the Performance of Artificial Intelligence in Chest Radiography.Journal of imaging · 2025Article
- Improving deep learning U-Net++ by discrete wavelet and attention gate mechanisms for effective pathological lung segmentation in chest X-ray imaging.Physical and engineering sciences in medicine · 2025Article
- Advanced federated ensemble internet of learning approach for cloud based medical healthcare monitoring system.Scientific reports · 2024Article
- Automated thorax disease diagnosis using multi-branch residual attention network.Scientific reports · 2024Article
- A deep learning feature extraction-based hybrid approach for detecting pediatric pneumonia in chest X-ray images.Physical and engineering sciences in medicine · 2024Article
- Article
- A retrospective longitudinal assessment of artificial intelligence-assisted radiographic prediction of lower third molar eruption.Scientific reports · 2024Article
- Data-driven classification and explainable-AI in the field of lung imaging.Frontiers in big data · 2024Review
- Computer image analysis with artificial intelligence: a practical introduction to convolutional neural networks for medical professionals.Postgraduate medical journal · 2023Review
- A lightweight CORONA-NET for COVID-19 detection in X-ray images.Expert systems with applications · 2023Article
- An Efficient, Lightweight, Tiny 2D-CNN Ensemble Model to Detect Cardiomegaly in Heart CT Images.Journal of personalized medicine · 2023Article
Corrections and comments
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Authors and funding
5 authors at 5 institutions in 3 countries.
Funding
Abstract
Pneumonia causes the death of around 700,000 children every year and affects 7% of the global population. Chest X-rays are primarily used for the diagnosis of this disease. However, even for a trained radiologist, it is a challenging task to examine chest X-rays. There is a need to improve the diagnosis accuracy. In this work, an efficient model for the detection of pneumonia trained on digital chest X-ray images is proposed, which could aid the radiologists in their decision making process. A novel approach based on a weighted classifier is introduced, which combines the weighted predictions from the state-of-the-art deep learning models such as ResNet18, Xception, InceptionV3, DenseNet121, and MobileNetV3 in an optimal way. This approach is a supervised learning approach in which the network predicts the result based on the quality of the dataset used. Transfer learning is used to fine-tune the deep learning models to obtain higher training and validation accuracy. Partial data augmentation techniques are employed to increase the training dataset in a balanced way. The proposed weighted classifier is able to outperform all the individual models. Finally, the model is evaluated, not only in terms of test accuracy, but also in the AUC score. The final proposed weighted classifier model is able to achieve a test accuracy of 98.43% and an AUC score of 99.76 on the unseen data from the Guangzhou Women and Children's Medical Center pneumonia dataset. Hence, the proposed model can be used for a quick diagnosis of pneumonia and can aid the radiologists in the diagnosis process.
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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.