Artificial intelligence in the healthcare sector: comparison of deep learning networks using chest X-ray images
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View abstract on PubMed
Summary
This summary is machine-generated.Deep learning models accurately diagnosed COVID-19 and pneumonia from chest X-rays. The ResNet101 model achieved the highest diagnostic success rate at 96.32%.
Area Of Science
- Medical Imaging
- Artificial Intelligence in Healthcare
- Deep Learning
Background
- Artificial intelligence (AI) is revolutionizing healthcare.
- Deep learning, a subset of AI, shows significant potential in medical applications.
Purpose Of The Study
- To evaluate the efficacy of deep learning networks in diagnosing diseases from chest X-rays.
- To compare the performance of ResNet101, AlexNet, GoogLeNet, and Xception for disease classification.
Main Methods
- A dataset of 1,680 chest X-rays was used, including COVID-19, viral pneumonia, and normal cases.
- Data augmentation via rotation was applied.
- A 70/30% split for training and validation was implemented.
- Four deep learning networks (ResNet101, AlexNet, GoogLeNet, Xception) were analyzed.
Main Results
- Deep learning networks demonstrated high success in classifying COVID-19, viral pneumonia, and normal chest X-rays.
- ResNet101 achieved the highest accuracy at 96.32%.
- The models proved effective in differentiating between disease states.
Conclusions
- Deep learning is a viable tool for disease diagnosis in healthcare.
- AI-powered diagnostics can support medical professionals and improve healthcare management.
- This technology can enhance diagnostic accuracy and efficiency.
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