Optimized Silicosis Detection Using a Feature-Enriched Multi-Model Ensemble Framework with An Enhanced U-Net Architecture

  • Authors

    • Shivaanivarsha. N Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Chennai, India and Department of Electronics and Communication Engineering, Sri Sairam Engineering College, Chennai, India
    • Kavipriya. P Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Chennai, India
    https://doi.org/10.14419/3gepr148

    Received date: August 23, 2025

    Accepted date: October 23, 2025

    Published date: November 5, 2025

  • Crystalline silica, Deep learning, Ensemble Model, Lung disease, Silicosis, U-Net

  • Abstract

    Silicosis remains a major occupational health concern worldwide. This Research proposes an ensemble learning model combined with an improved U-Net for silicosis detection and classification. The dataset included 1300 healthy lung images and 1328 augmented silicosis images, split 80:20 for training and testing. The ensemble model integrated Inception V2, Inception V3, MobileNet, and ResNet50 for robust classification. Enhanced U-Net with skip connections and optimized hyperparameters achieved a validation accuracy of 98.33% and a Dice coefficient of 0.967. The ensemble outperformed standalone models, achieving an F1-score of 95.57%, a precision of 96.42%, and an MCC of 0.9275. Training was conducted using an NVIDIA i7 CPU, with R-Studio for analytics and visualization. Comparative analysis confirmed the model's superiority, aiding radiologists in faster decision-making.

  • References

    1. Shivaanivarsha, N., & Kavipriya, P. (2023). Identification of silicosis using deep learning model on computed tomography images. In IEEE 3rd My-sore Sub Section International Conference (MysuruCon), Mysuru, India.
    2. Liu, Y., Yu, L., Wu, Q., & Zhang, X. (2024). Identification of high-risk population of pneumoconiosis using deep learning segmentation of lung 3D images and radiomics texture analysis. Computer Methods and Programs in Biomedicine, 244, 108006.
    3. Li, X., Zheng, Y., Li, Y., & Zhang, Z. (2024). Deep convolutional network-based chest radiographs screening model for pneumoconiosis. Frontiers in Medicine, 11, 1290729.
    4. Devnath, L., Kumar, R., Singh, P., & Pandey, A. (2022). Detection and visualisation of pneumoconiosis using an ensemble of multi-dimensional deep features learned from chest X-rays. International Journal of Environmental Research and Public Health, 19(18), 11193.
    5. Devnath, L., Kumar, R., Singh, P., & Pandey, A. (2022). Deep ensemble learning for the automatic detection of pneumoconiosis in coal worker’s chest X-ray radiography. Journal of Clinical Medicine, 11(18), 5342.
    6. Wang, D., Zhang, Y., Liu, J., & Zhang, J. (2020). Automated pneumoconiosis detection on chest X-rays using cascaded learning with real and syn-thetic radiographs. In Proceedings of the Digital Image Computing: Techniques and Applications (DICTA).
    7. Xuan, W., Han, Z., & Zheng, L. (2021). Non-invasive detection of silicosis based on array sensing and pattern recognition. E3S Web of Conferences, 271, 1–6.
    8. Yang, F., Zhi, M., Chen, X., & Li, Y. (2021). Pneumoconiosis computer aided diagnosis system based on X-rays and deep learning. BMC Medical Imaging, 21, 1–7.
    9. Gao, Q., Wang, S., Zhao, D., & Liu, J. (2007). Accurate lung segmentation for X-ray CT images. In Proceedings of the Third International Confer-ence on Natural Computation, Haikou, China, 275–279.
    10. Chanda, P. B., & Sarkar, S. K. (2020). Effective and reliable lung segmentation of chest images with medical image processing and machine learn-ing approaches. In IEEE International Conference on Advent Trends in Multidisciplinary Research and Innovation (ICATMRI), Buldhana, India.
    11. Saad, M. N., Mohsin, M. F., Hamid, H. A., & Muda, Z. (2021). Lung area segmentation on chest X-ray based on texture image feature. In Proceed-ings of the 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), Penang, Malaysia, 92–96.
    12. Zhang, L., Rong, R., Li, Q., & Wang, X. (2021). A deep learning-based model for screening and staging pneumoconiosis. Scientific Reports, 11(2201), 1–7.
    13. Shivaanivarsha, N., & Kavipriya, P. (2023). A robust approach for the detection of diabetic retinopathy at an early stage using deep CNN. In Pro-ceedings of the International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, Chennai, India.
    14. Ramesh, A., Bhatkhande, A., Avduth, N., Jadhav, B., & Rathi, K. (2022). Automated lung segmentation on chest CT using deep learning. In Pro-ceedings of the 3rd International Conference for Emerging Technology (INCET), Belgaum, India.
    15. Shivaanivarsha, N., Kavipriya, P., & Shyamkumar, M. (2023). An efficient fully automated detection of mucormycosis using three-dimensional deep learning on computer tomography studies. In Proceedings of the International Conference on Recent Advances in Electrical, Electronics, Ubiq-uitous Communication, and Computational Intelligence (RAEEUCCI), Chennai, India.

  • Downloads

  • How to Cite

    N , S. ., & P , K. . (2025). Optimized Silicosis Detection Using a Feature-Enriched Multi-Model Ensemble Framework with An Enhanced U-Net Architecture. International Journal of Basic and Applied Sciences, 14(7), 149-160. https://doi.org/10.14419/3gepr148