ArticleMultimedia tools and applications2022
A lightweight deep learning architecture for the automatic detection of pneumonia using chest X-ray images.
Article in Multimedia tools and applications, 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.
- Respiratory Disease Detection: A Systematic Review of AI-Based Approaches, from Audio and Visual Unimodal Methods to Multimodal Integration.Diagnostics (Basel, Switzerland) · 2026Review
- A Deep Learning-Based EffConvNeXt Model for Automatic Classification of Cystic Bronchiectasis: An Explainable AI Approach.Journal of imaging informatics in medicine · 2026Article
- FLPneXAINet: Federated deep learning and explainable AI for improved pneumonia prediction utilizing GAN-augmented chest X-ray data.PloS one · 2025Article
- Research on Three-Dimensional Reconstruction of Ribs Based on Point Cloud Adaptive Smoothing Denoising.Sensors (Basel, Switzerland) · 2024Article
- A Deep Convolutional Neural Network for Pneumonia Detection in X-ray Images with Attention Ensemble.Diagnostics (Basel, Switzerland) · 2024Article
- A multi-class classification framework for disease screening and disease diagnosis of COVID-19 from chest X-ray images.Multimedia tools and applications · 2023Article
- Pediatric pneumonia diagnosis using stacked ensemble learning on multi-model deep CNN architectures.Multimedia tools and applications · 2023Article
- LWSNet - a novel deep-learning architecture to segregate Covid-19 and pneumonia from x-ray imagery.Multimedia tools and applications · 2023Article
- CDC_Net: multi-classification convolutional neural network model for detection of COVID-19, pneumothorax, pneumonia, lung Cancer, and tuberculosis using chest X-rays.Multimedia tools and applications · 2023Article
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2 authors.
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Abstract
Pneumonia is a life-threatening respiratory lung disease. Children are more prone to be affected by the disease and accurate manual detection is not easy. Generally, chest radiographs are used for the manual detection of pneumonia and expert radiologists are required for the assessment of the X-ray images. An automatic system would be beneficial for the diagnosis of pneumonia based on chest radiographs as manual detection is time-consuming and tedious. Therefore, a method is proposed in this paper for the fast and automatic detection of pneumonia. A deep learning-based architecture 'MobileNet' is proposed for the automatic detection of pneumonia based on the chest X-ray images. A benchmark dataset of 5856 chest X-ray images was taken for the training, testing, and evaluation of the proposed deep learning network. The proposed model was trained within 3 Hrs. and achieved a training accuracy of 97.34%, a validation accuracy of 87.5%, and a testing accuracy of 94.23% for automatic detection of pneumonia. However, the combined accuracy was achieved as 97.09% with 0.96 specificity, 0.97 precision, 0.98 recall, and 0.97 F-Score. The proposed method was found faster and computationally lesser expensive as compared to other methods in the literature and achieved a promising accuracy.
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