ArticleHealthcare (Basel, Switzerland)2023
Detection of Pneumonia from Chest X-ray Images Utilizing MobileNet Model.
Article in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 189 citations in OpenAlex.
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- A Deep Learning-Based EffConvNeXt Model for Automatic Classification of Cystic Bronchiectasis: An Explainable AI Approach.Journal of imaging informatics in medicine · 2026Article
- Development and evaluation of a multistage transfer learning framework for robust medical image analysis.Scientific reports · 2026Article
- Artificial Intelligence Meets Nail Diagnostics: Emerging Image-Based Sensing Platforms for Non-Invasive Disease Detection.Bioengineering (Basel, Switzerland) · 2026Review
- Application of deep learning methods in the classification of normal and pneumonia lung images.Frontiers in medicine · 2026Article
- HybridCATNet: an explainable CNN-Transformer framework with early multi-channel fusion and uncertainty estimation for pneumonia detection from chest X-rays.Frontiers in digital health · 2026Article
- Efficient and Accurate Pneumonia Detection Using a Novel Multi-Scale Transformer Approach.Sensors (Basel, Switzerland) · 2025Article
- COVID-19 Pneumonia Diagnosis Using Medical Images: Deep Learning-Based Transfer Learning Approach.JMIRx med · 2025Article
- PneumoNet: Deep Neural Network for Advanced Pneumonia Detection.Current medical imaging · 2025Article
- Enhanced ResNet-50 with Multi-Feature Fusion for Robust Detection of Pneumonia in Chest X-Ray Images.Diagnostics (Basel, Switzerland) · 2025Article
- SqueezeViX-Net with SOAE: A Prevailing Deep Learning Framework for Accurate Pneumonia Classification using X-Ray and CT Imaging Modalities.Current medical imaging · 2025Article
- PneuNet: a lightweight convolutional neural network with multiscale feature fusion for automated pneumonia detection from chest X-rays.Frontiers in medicine · 2025Article
- Optimization of convolutional neural network and visual geometry group-16 using genetic algorithms for pneumonia detection.Frontiers in medicine · 2024Article
- Pneumonia detection on chest X-rays from Xception-based transfer learning and logistic regression.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Article
- YOLOv8 framework for COVID-19 and pneumonia detection using synthetic image augmentation.Digital healthArticle
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
Abstract
Pneumonia has been directly responsible for a huge number of deaths all across the globe. Pneumonia shares visual features with other respiratory diseases, such as tuberculosis, which can make it difficult to distinguish between them. Moreover, there is significant variability in the way chest X-ray images are acquired and processed, which can impact the quality and consistency of the images. This can make it challenging to develop robust algorithms that can accurately identify pneumonia in all types of images. Hence, there is a need to develop robust, data-driven algorithms that are trained on large, high-quality datasets and validated using a range of imaging techniques and expert radiological analysis. In this research, a deep-learning-based model is demonstrated for differentiating between normal and severe cases of pneumonia. This complete proposed system has a total of eight pre-trained models, namely, ResNet50, ResNet152V2, DenseNet121, DenseNet201, Xception, VGG16, EfficientNet, and MobileNet. These eight pre-trained models were simulated on two datasets having 5856 images and 112,120 images of chest X-rays. The best accuracy is obtained on the MobileNet model with values of 94.23% and 93.75% on two different datasets. Key hyperparameters including batch sizes, number of epochs, and different optimizers have all been considered during comparative interpretation of these models to determine the most appropriate model.
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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.