ArticleInformation sciences2022
Deep features to detect pulmonary abnormalities in chest X-rays due to infectious diseaseX: Covid-19, pneumonia, and tuberculosis.
Article in Information sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Performance of Artificial Intelligence-Based Methods for Tuberculosis Detection: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Artificial intelligence in respiratory medicine: From diagnosis to treatment and future directions.Chinese medical journal pulmonary and critical care medicine · 2026Review
- Capuchin Red Kite–optimized Swin Transformer-based Convolutional Block Attention Module for Early Diagnosis and Classification of PneumoniaCurrent medical imaging · 2026Article
- DETECTX: a novel deep learning approach for detecting pneumonia, tuberculosis, and aspergillosis from chest X-rays.Frontiers in cellular and infection microbiology · 2026Article
- NextGen lung disease diagnosis with explainable artificial intelligence.Scientific reports · 2025Article
- Detection of COVID-19, pneumonia, and tuberculosis from radiographs using AI-driven knowledge distillation.Heliyon · 2024Article
- Early user perspectives on using computer-aided detection software for interpreting chest X-ray images to enhance access and quality of care for persons with tuberculosis.BMC global and public health · 2023Review
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
- Combating Covid-19 using machine learning and deep learning: Applications, challenges, and future perspectives.Array (New York, N.Y.) · 2023Review
- Selective Deeply Supervised Multi-Scale Attention Network for Brain Tumor Segmentation.Sensors (Basel, Switzerland) · 2023Article
- SecureFed: federated learning empowered medical imaging technique to analyze lung abnormalities in chest X-rays.International journal of machine learning and cybernetics · 2023Article
- Governing AI during a pandemic crisis: Initiatives at the EU level.Technology in society · 2023Article
- Analyzing Overlaid Foreign Objects in Chest X-rays-Clinical Significance and Artificial Intelligence Tools.Healthcare (Basel, Switzerland) · 2023Review
- A Novel Multi-Task Learning Network Based on Melanoma Segmentation and Classification with Skin Lesion Images.Diagnostics (Basel, Switzerland) · 2023Article
- Detection of sarcopenia using deep learning-based artificial intelligence body part measure system (AIBMS).Frontiers in physiology · 2023Article
- COVIDX-LwNet: A Lightweight Network Ensemble Model for the Detection of COVID-19 Based on Chest X-ray Images.Sensors (Basel, Switzerland) · 2022Article
- Explanatory classification of CXR images into COVID-19, Pneumonia and Tuberculosis using deep learning and XAI.Computers in biology and medicine · 2022Article
- Deep viewing for the identification of Covid-19 infection status from chest X-Ray image using CNN based architecture.Intelligent systems with applications · 2022Article
- An efficient deep neural network framework for COVID-19 lung infection segmentation.Information sciences · 2022Article
- Detecting COVID-19 infection status from chest X-ray and CT scanFrontiers in genetics · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chest X-ray (CXR) imaging is a low-cost, easy-to-use imaging alternative that can be used to diagnose/screen pulmonary abnormalities due to infectious diseaseX: Covid-19, Pneumonia and Tuberculosis (TB). Not limited to binary decisions (with respect to healthy cases) that are reported in the state-of-the-art literature, we also consider non-healthy CXR screening using a lightweight deep neural network (DNN) with a reduced number of epochs and parameters. On three diverse publicly accessible and fully categorized datasets, for non-healthy versus healthy CXR screening, the proposed DNN produced the following accuracies: 99.87% on Covid-19 versus healthy, 99.55% on Pneumonia versus healthy, and 99.76% on TB versus healthy datasets. On the other hand, when considering non-healthy CXR screening, we received the following accuracies: 98.89% on Covid-19 versus Pneumonia, 98.99% on Covid-19 versus TB, and 100% on Pneumonia versus TB. To further precisely analyze how well the proposed DNN worked, we considered well-known DNNs such as ResNet50, ResNet152V2, MobileNetV2, and InceptionV3. Our results are comparable with the current state-of-the-art, and as the proposed CNN is light, it could potentially be used for mass screening in resource-constraint regions.
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