ARCHIVES

[1] L. Yang, Y. Wan, and F. Pan, “Enhancing chest X-ray diagnosis with a multimodal deep learning network by integrating clinical history to refine attention,” Journal of Imaging Informatics in Medicine, vol. 38, pp. 3568–3583, 2025, doi: 10.1007/s10278-025-01446-1.
[2] S. M. A. I. Saky, M. R. Islam, M. S. Arefin, and S. Alam, “Explainable multi-modal deep learning for automatic detection of lung diseases from respiratory audio signals,” arXiv:2512.00563, 2025.
[3] T. M. Alanazi, “Multimodal feature distinguishing and deep learning approach to detect lung disease from MRI images,” Scientific Reports, vol. 15, art. 31932, 2025, doi: 10.1038/s41598-025-17796-3.
[4] N. Veeramani et al., “NextGen lung disease diagnosis with explainable artificial intelligence,” Scientific Reports, vol. 15, art. 33052, 2025, doi: 10.1038/s41598-025-07603-4.
[5] “A multimodal AI framework for automated multiclass lung disease diagnosis from respiratory sounds with simulated biomarker fusion and personalized medication recommendation,” International Journal of Molecular Sciences, vol. 26, no. 15, p. 7135, 2025, doi: 10.3390/ijms26157135.
[6] X. Tan, F. Pan, N. Zhan et al., “Multimodal integration to identify the invasion status of lung adenocarcinoma intraoperatively,” iScience, vol. 27, p. 111421, 2024, doi: 10.1016/j.isci.2024.111421.
[7] S. Sarker, S. R. Refat, F. F. Preotee, T. R. Shawon, and R. Tanvir, “Comprehensive lung disease detection using deep learning models and hybrid chest X-ray data with explainable AI,” in Proc. 2024 27th International Conference on Computer and Information Technology (ICCIT), 2024, doi: 10.1109/ICCIT64611.2024.11022605.
[8] V. I. Agughasi, “Leveraging transfer learning for efficient diagnosis of COPD using CXR images and explainable AI techniques,” Inteligencia Artificial, vol. 27, no. 74, pp. 133–151, 2024, doi: 10.4114/intartif.vol27iss74pp133-151.
[9] “MultiFusionNet: Multilayer multimodal fusion of deep neural networks for chest X-ray image classification,” arXiv:2401.00728, 2024.
[10] “Multilabel classification for lung disease detection: Integrating deep learning and natural language processing,” arXiv:2412.11452, 2024.
[11] M. H. Al-Sheikh, O. Al Dandan, A. S. Al-Shamayleh, H. A. Jalab, and R. W. Ibrahim, “Multi-class deep learning architecture for classifying lung diseases from chest X-Ray and CT images,” Scientific Reports, vol. 13, art. 19452, 2023, doi: 10.1038/s41598-023-46147-3.
[12] C. C. Ukwuoma et al., “A hybrid explainable ensemble transformer encoder for pneumonia identification from chest X-ray images,” Journal of Advanced Research, vol. 48, pp. 191–211, 2023, doi: 10.1016/j.jare.2022.08.021.
[13] Y. Chen, Y. Wan, and F. Pan, “Enhancing multi-disease diagnosis of chest X-rays with advanced deep-learning networks in real-world data,” Journal of Digital Imaging, vol. 36, pp. 1332–1347, 2023, doi: 10.1007/s10278-023-00801-4.
[14] M. Bhandari, T. B. Shahi, B. Siku, and A. Neupane, “Explanatory classification of CXR images into COVID-19, pneumonia and tuberculosis using deep learning and XAI,” Computers in Biology and Medicine, vol. 150, p. 106156, 2022, doi: 10.1016/j.compbiomed.2022.106156.
[15] M. K. Mahbub, M. Biswas, L. Gaur, F. Alenezi, and K. Santosh, “Deep features to detect pulmonary abnormalities in chest X-rays due to infectious diseases: COVID-19, pneumonia, and tuberculosis,” Information Sciences, vol. 592, pp. 389–401, 2022, doi: 10.1016/j.ins.2022.01.062.
[16] N. Sharma, L. Saba, N. N. Khanna, M. K. Kalra, M. M. Fouda, and J. S. Suri, “Segmentation-based classification deep learning model embedded with explainable AI for COVID-19 detection in chest X-ray scans,” Diagnostics, vol. 12, no. 9, p. 2132, 2022, doi: 10.3390/diagnostics12092132.
[17] H. Zhang, C. Xu, and P. Liang et al., “MMLN: Leveraging domain knowledge for multimodal diagnosis,” in Bioinformatics Research and Applications, Springer Nature, 2022, pp. 192–203, doi: 10.1007/978-3-031-23198-8_18.
[18] E. Gasca Cervantes and W.-Y. Chan, “LIME-enabled investigation of convolutional neural network performances in COVID-19 chest X-ray detection,” Proc. 2021 IEEE Canadian Conference on Electrical and Computer Engineering, pp. 1–6, 2021, doi: 10.1109/CCECE53047.2021.9569228.
[19] S. Rahman, S. Sarker, M. A. Miraj et al., “Deep learning–driven automated detection of COVID-19 from radiography images: A comparative analysis,” Cognitive Computation, 2021, doi: 10.1007/s12559-020-09779-5.
[20] L. Brunese, F. Mercaldo, A. Reginelli, and A. Santone, “Explainable deep learning for pulmonary disease and coronavirus COVID-19 detection from X-rays,” Computer Methods and Programs in Biomedicine, vol. 196, p. 105608, 2020, doi: 10.1016/j.cmpb.2020.105608.
[21] R. R. Selvaraju et al., “Grad-CAM: Visual explanations from deep networks via gradient-based localization,” arXiv:1610.02391, 2016.
[22] “Artificial intelligence for chest radiography: an overview of techniques, challenges, and future directions,” npj Health Systems, 2026.
[23] “Explainable hybrid transformer for multi-classification of lung disease using chest X-rays,” Scientific Reports, 2025.
[24] Z. Qiu, H. Rivaz, and Y. Xiao, “Is visual explanation with Grad-CAM more reliable for deeper neural networks? A case study with automatic pneumothorax diagnosis,” arXiv:2308.15172, 2023.