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
HydraViT: Adaptive Multi-Branch Transformer for Multi-Label Disease Classification from Chest X-ray Images
Şaban Öztürk, M. Yiğit Turalı, Tolga Çukur
Advancing Diagnostic Precision: Leveraging Machine Learning Techniques for Accurate Detection of Covid-19, Pneumonia, and Tuberculosis in Chest X-Ray Images
Aditya Kulkarni, Guruprasad Parasnis, Harish Balasubramanian, Vansh Jain, Anmol Chokshi, Reena Sonkusare