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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 .
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

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 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