Open Access
Issue
EPJ Web Conf.
Volume 363, 2026
International Conference on Low-Carbon Development and Materials for Solar Energy (ICLDMS’26)
Article Number 02001
Number of page(s) 11
Section Engineering Materials
DOI https://doi.org/10.1051/epjconf/202636302001
Published online 16 April 2026
  1. Mohammed, A.A., Abdulwahhab, A.H. & Ibraheem, I.K., “Detection of Lung Nodules Using Medical CT Images Based on Deep Learning Techniques”, Baghdad Science Journal, 21(1), 2024, pp. 115–126. [Google Scholar]
  2. Suchitra, M., Patgar, T.M., Varsha, S.A. & Sudhamani, M.V., “Detection and Classification of Lung Nodules Using Deep Learning Methods”, International Journal of Engineering Research and Technology, 13(4), 2024, pp. 1–7. [Google Scholar]
  3. Alshamrani, S., Ahmed, M. & Hassan, R., “Classification of Chest CT Lung Nodules Using Collaborative Deep Learning Model”, Journal of Multidisciplinary Healthcare, 17, 2024, pp. 1567–1578. [Google Scholar]
  4. Mamun, M., Mahmud, M.I., Meherin, M. & Abdelgawad, A., “LCDctCNN: Lung Cancer Diagnosis of CT Scan Images Using CNN Based Model”, International Journal of Advanced Computer Science and Applications, 14(3), 2023, pp. 520–528. [Google Scholar]
  5. Drishti, D. & Singh, J., “Novel Algorithm for Pulmonary Nodule Classification Using CNN on CT Scans”, International Journal of Intelligent Systems and Applications in Engineering, 12(2), 2023, pp. 144–152. [Google Scholar]
  6. Kumar, R., Gupta, R. & Verma, S., “Deep Learning-Based Lung Cancer Detection Using Transfer Learning on CT Images”, Multimedia Tools and Applications, 82(9), 2023, pp. 13201–13219. [Google Scholar]
  7. Arora, S., Singh, L. & Sharma, P., “Lung Cancer Detection Using Deep Learning Techniques on CT Scan Images”, Biomedical Signal Processing and Control, 72, 2022, pp. 103306. [Google Scholar]
  8. Wang, S., Zhou, M., Liu, Z. & Liu, Z., “Central Focused Convolutional Neural Networks for Lung Nodule Classification”, IEEE Transactions onMedical Imaging, 41(6), 2022, pp. 1501–1512. [Google Scholar]
  9. Zhang, G., Yang, Z., Gong, Y. & Jiang, X., “Pulmonary Lung Nodule Detection from Computed Tomography Images Using Two-Stage Convolutional Neural Network”, The Computer Journal, 66(4), 2021, pp. 785–798. [Google Scholar]
  10. Khan, M.A., Sharif, M., Akram, T. & Kadry, S., “A Deep Learning Framework for Automatic Detection of Lung Nodules from CT Images”, Computers in Biology and Medicine, 128, 2021, pp. 104099. [Google Scholar]
  11. Masood, A., Sheng, B., Li, P., Hou, X., Wei, X. & Qin, J., “Computer-Assisted Decision Support System in Pulmonary Cancer Detection Using Deep Learning”, Journal of Medical Systems, 45(5), 2021, pp. 53. [Google Scholar]
  12. Halder, A., Dey, D. & Sadhu, A.K., “Lung Nodule Detection from Feature Engineering to Deep Learning in Thoracic CT Images: A Comprehensive Review”, Journal of Digital Imaging, 33(3), 2020, pp. 655–677. [Google Scholar]
  13. Xu, Y.M., Zhang, T., Xu, H., Qi, L., Zhang, W., Gao, D.S. & Yuan, M., “Deep Learning in CT Images: Automated Pulmonary Nodule Detection for Subsequent Management Using Convolutional Neural Network”, Cancer Managementand Research, 12, 2020, pp. 1217–1228. [Google Scholar]
  14. Zheng, H., Wang, Y. & Zhao, L., “Deep Convolutional Neural Networks for Multi-Planar Lung Nodule Detection”, IEEE Access, 8, 2020, pp. 178902–178912. [Google Scholar]
  15. Nasrullah, N., Sang, J., Alam, M., Mateen, M., Cai, B. & Hu, H., “Automated Lung Nodule Detection and Classification Using Deep Learning Combined with Multiple Strategies”, Sensors, 19(17), 2019, pp. 3722–3735. [Google Scholar]
  16. Kooi, T., “A comparison between a deep convolutional neural network and radiologists for classifying regions of interest in mammography”, Medical Physics, 44(6), 2017, pp. 2567–2576. [Google Scholar]
  17. Wang, S., “Deep learning for identifying radiogenomic associations in breast cancer”, Nature Scientific Reports, 8(1), 2018, pp. 13039–13052. [Google Scholar]
  18. Shen, W., “Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification”, Pattern Recognition, 61, 2017, pp. 663–673. [Google Scholar]
  19. Hua, K.L., “Computer-aided classification of lung nodules on computed tomography images via deep learning technique”, OncoTargets and Therapy, 8, 2015, pp. 2015–2022. [Google Scholar]
  20. Ciompi, F., “Towards automatic pulmonary nodule management in lung cancer screening with deep learning”, Scientific Reports, 7(1), 2017, pp. 46479–46490. [Google Scholar]
  21. Xie, Y., “Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification”, IEEE Transactions on Biomedical Engineering, 66(1), 2019, pp. 90–102. [Google Scholar]
  22. Kumar, D., “A Hybrid Framework for Lung Nodule Detection in CT Images Using CNN and Handcrafted Features”, Journal of Digital Imaging, 32(3), 2019, pp. 405–415. [Google Scholar]
  23. Liang, M., “Low-Dose CT Screening for Lung Cancer: Computer-aided Detection of Missed Lung Cancers”, Radiology, 281(1), 2016, pp. 279–288. [Google Scholar]
  24. Setio, A.A.A., “Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks”, IEEE Transactions onMedical Imaging, 35(5), 2016, pp. 1160–1169. [Google Scholar]
  25. Anthimopoulos, M., “Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network”, IEEE Transactions onMedical Imaging, 35(5), 2016, pp. 1207–1216. [Google Scholar]
  26. Gao, M., “Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks”, Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 6(1), 2018, pp. 1–6. [Google Scholar]
  27. Teramoto, A., “Automated classification of pulmonary nodules through a retrospective analysis of conventional CT and two-phase PET images in patients undergoing biopsy”, American Journal of Roentgenology, 211(1), 2018, pp. 25–31. [Google Scholar]
  28. Liu, X., “Artificial Intelligence CT Screening Model for Lung Cancer Risk Prediction”, Chest, 158(6), 2020, pp. 2493–2502. [Google Scholar]

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