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Published on in Vol 4 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67144, first published .
Doctor examining a chest X-ray, reviewing medical imaging for diagnosis

Comparison of Deep Learning Approaches Using Chest Radiographs for Predicting Clinical Deterioration: Retrospective Observational Study

Comparison of Deep Learning Approaches Using Chest Radiographs for Predicting Clinical Deterioration: Retrospective Observational Study

Journals

  1. Singthongchai J, Wangkhamhan T. Adaptive Normalization Enhances the Generalization of Deep Learning Model in Chest X-Ray Classification. Journal of Imaging 2025;12(1):14 View
  2. Kim J. Explainable deep learning-based multiclass classification of foot radiographs into normal, plantar fasciitis, and flatfoot. Clinical Imaging 2026;131:110724 View
  3. Yildirim A, Canayaz M. Lumbar‐DAFSNet: Densely‐Attended Feature Selection Network for Lumbar Disc Herniation Detection. Concurrency and Computation: Practice and Experience 2026;38(7) View
  4. Anbukkarasi S, Hemalatha S, Balakrishnan A, Varadhaganapathy S, Easwaramoorthy S. K-STAMM: a knowledge-enhanced spatial – temporal attention model with multimodal fusion for pneumonia prediction. Scientific Reports 2026;16(1) View
  5. Muringathuparambil J, Maharaj S, Segal B, Mahomed N. Artificial intelligence in paediatric chest imaging: applications, challenges, and future directions. Pediatric Radiology 2026 View

Conference Proceedings

  1. Gonthina S, Shahna Amrilly S, Maddur T, Lakshmi Akella P. 2025 IEEE 22nd India Council International Conference (INDICON). Adaptive Cross-View Attention with Interpretable Bidirectional Lightweight Sequencing for Chest Radiography View