Joint Diagnosis of Pneumonia, COVID-19, and Tuberculosis from Chest X-ray Images: A Deep Learning Approach
Problem
Jointly detecting pneumonia, COVID-19 and tuberculosis from chest X-rays, since patients may have more than one lung disease at once.
Why it matters
These are among the most fatal and common lung diseases, and single-disease screening can miss co-occurring conditions.
What the AI did
A deep learning model was trained on multiple public Kaggle chest X-ray datasets to classify pneumonia, COVID-19, tuberculosis and no-findings jointly.
Results
98.72% accuracy overall; recall 99.66% pneumonia, 99.35% no-findings, 98.10% tuberculosis (abstract truncated).
Impact
High benchmark accuracy on public datasets; clinical validation or deployment not stated in source.
Record
| Venue | Diagnostics |
|---|---|
| Published | 2023 |
| Work type | article |
| Authors | Mohammed Salih Ahmed, Atta Rahman, Faris AlGhamdi, Saleh AlDakheel, Hammam Hakami, Ali AlJumah, Zuhair AlIbrahim, Mustafa M. Youldash et al. |
| Citations | 86 (OpenAlex, at import time) |
| References | 47 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/diagnostics13152562 |
| Open access | gold |
| Keywords | chest X-ray, deep learning, pneumonia, COVID-19, tuberculosis, joint diagnosis |
Importance rating
significant — thousands to millions affected
Abstract calls pneumonia, COVID-19 and TB some of the most fatal and common lung diseases; joint detection addresses missed co-occurring conditions, though no scale numbers are given.