Chest X Ray Image
Chest X-ray images are crucial for diagnosing various lung diseases, and research focuses on automating their analysis using artificial intelligence. Current efforts center on developing and refining deep learning models, including convolutional neural networks (CNNs) like ResNet and DenseNet, vision transformers (ViTs), and diffusion models, to improve the accuracy and efficiency of disease detection and localization, often incorporating techniques like transfer learning, attention mechanisms, and multimodal fusion with electronic health records. This work holds significant promise for improving diagnostic accuracy, reducing radiologist workload, and enabling faster, more accessible healthcare, particularly in resource-constrained settings.
Papers
Classification of COVID-19 in Chest X-ray Images Using Fusion of Deep Features and LightGBM
Hamid Nasiri, Ghazal Kheyroddin, Morteza Dorrigiv, Mona Esmaeili, Amir Raeisi Nafchi, Mohsen Haji Ghorbani, Payman Zarkesh-Ha
SwinCheX: Multi-label classification on chest X-ray images with transformers
Sina Taslimi, Soroush Taslimi, Nima Fathi, Mohammadreza Salehi, Mohammad Hossein Rohban