ArticleDiagnostics (Basel, Switzerland)2023
Joint Diagnosis of Pneumonia, COVID-19, and Tuberculosis from Chest X-ray Images: A Deep Learning Approach.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Enhancing lung diseases recognition through CNN-RNN methodologies.Scientific reports · 2026Article
- A Lightweight Hybrid Deep Learning Model for Tuberculosis Detection from Chest X-Rays.Diagnostics (Basel, Switzerland) · 2025Article
- Adaptive segmentation for accurate detection of tuberculosis disorder using multiscale residual densenet with attention mechanism by analyzing the chest X-ray images.Journal of clinical tuberculosis and other mycobacterial diseases · 2025Article
- Machine Learning and Artificial Intelligence for Infectious Disease Surveillance, Diagnosis, and Prognosis.Viruses · 2025Review
- SqueezeViX-Net with SOAE: A Prevailing Deep Learning Framework for Accurate Pneumonia Classification using X-Ray and CT Imaging Modalities.Current medical imaging · 2025Article
- Exploring the efficacy of GRU model in classifying the signal to noise ratio of microgrid model.Scientific reports · 2024Article
- TSSG-CNN: A Tuberculosis Semantic Segmentation-Guided Model for Detecting and Diagnosis Using the Adaptive Convolutional Neural Network.Diagnostics (Basel, Switzerland) · 2024Article
- Toward the novel AI tasks in infection biology.mSphere · 2024Review
- Assessing Acetabular Index Angle in Infants: A Deep Learning-Based Novel Approach.Journal of imaging · 2023Article
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Authors and funding
10 authors.
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Abstract
Pneumonia, COVID-19, and tuberculosis are some of the most fatal and common lung diseases in the current era. Several approaches have been proposed in the literature for the diagnosis of individual diseases, since each requires a different feature set altogether, but few studies have been proposed for a joint diagnosis. A patient being diagnosed with one disease as negative may be suffering from the other disease, and vice versa. However, since said diseases are related to the lungs, there might be a likelihood of more than one disease being present in the same patient. In this study, a deep learning model that is able to detect the mentioned diseases from the chest X-ray images of patients is proposed. To evaluate the performance of the proposed model, multiple public datasets have been obtained from Kaggle. Consequently, the proposed model achieved 98.72% accuracy for all classes in general and obtained a recall score of 99.66% for Pneumonia, 99.35% for No-findings, 98.10% for Tuberculosis, and 96.27% for COVID-19, respectively. Furthermore, the model was tested using unseen data from the same augmented dataset and was proven to be better than state-of-the-art studies in the literature in terms of accuracy and other metrics.
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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 Socratic graph.