ArticleApplied soft computing2022
Chest X-ray analysis empowered with deep learning: A systematic review.
Article in Applied soft computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Intelligent Real-Time Healthcare and Biomedical Monitoring Systems: A Narrative Review of AI, IoT, and Emerging Technologies.Bioengineering (Basel, Switzerland) · 2026Review
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- KD-SqueezeNet: an efficient deep learning strategy for the multi-task diagnosis of neonatal lung diseases.Pediatric radiology · 2026Article
- Explainable AI for chest radiographs: sex-stratified fairness auditing in CNN-based pneumonia detection.Scientific reports · 2026Article
- Diagnostic performance of an artificial intelligence algorithm for detecting pneumoperitoneum on abdominal CT scans.Insights into imaging · 2026Article
- Towards Automated Quality Assurance: Integrating Deep Learning and Classical ML into the Digital Radiography Pipeline.Diagnostics (Basel, Switzerland) · 2026Article
- CCEO-DCABNet: Chronological Chaotic Evolution Optimization-Enabled Hybrid Deep Learning for Multiclass Disease Classification Using Chest X-Ray Images in Federated Learning.Diagnostics (Basel, Switzerland) · 2026Article
- Effect of Irreversible Compression on the Pulmonary Nodule Detection Rate in Chest Radiographs Using AI Software.Diagnostics (Basel, Switzerland) · 2026Article
- Evidence-Guided Diagnostic Reasoning for Pediatric Chest Radiology Based on Multimodal Large Language Models.Journal of imaging · 2026Article
- Intelligent Attention-Driven Deep Learning for Hip Disease Diagnosis: Fusing Multimodal Imaging and Clinical Text for Enhanced Precision and Early Detection.Medicina (Kaunas, Lithuania) · 2026Article
- HyRA-CXR: a hybrid residual-attention deep network for chest X-ray classification.Frontiers in artificial intelligence · 2026Article
- Cutting-edge bayesian deep learning and statistical strategies for bias mitigation in COVID-19 detection via chest x-ray imaging.Scientific reports · 2025Article
- Detection of subclinical atherosclerosis by image-based deep learning on chest X-ray.European heart journal. Digital health · 2025Article
- Review
- Intelligent health model for medical imaging to guide laymen using neural cellular automata.Scientific reports · 2025Article
- Article
- Advanced imaging techniques and artificial intelligence in pleural diseases: a narrative review.European respiratory review : an official journal of the European Respiratory Society · 2025Review
- Computer-aided diagnosis for China-Japan Friendship Hospital classification of necrotic femurs using statistical shape and appearance model based on CT scans.Medical & biological engineering & computing · 2025Article
- Approach for enhancing the accuracy of semantic segmentation of chest X-ray images by edge detection and deep learning integration.Frontiers in artificial intelligence · 2025Article
- A hybrid dense convolutional network and fuzzy inference system for pneumonia diagnosis with dynamic symptom tracking.PloS one · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chest radiographs are widely used in the medical domain and at present, chest X-radiation particularly plays an important role in the diagnosis of medical conditions such as pneumonia and COVID-19 disease. The recent developments of deep learning techniques led to a promising performance in medical image classification and prediction tasks. With the availability of chest X-ray datasets and emerging trends in data engineering techniques, there is a growth in recent related publications. Recently, there have been only a few survey papers that addressed chest X-ray classification using deep learning techniques. However, they lack the analysis of the trends of recent studies. This systematic review paper explores and provides a comprehensive analysis of the related studies that have used deep learning techniques to analyze chest X-ray images. We present the state-of-the-art deep learning based pneumonia and COVID-19 detection solutions, trends in recent studies, publicly available datasets, guidance to follow a deep learning process, challenges and potential future research directions in this domain. The discoveries and the conclusions of the reviewed work have been organized in a way that researchers and developers working in the same domain can use this work to support them in taking decisions on their research.
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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 OpenQuestion graph.