Long-term exposure to crystalline silica dust causes silicosis, an irreversible occupational lung disease that is currently a major global health concern because of its delayed diagnosis and few available treatments. In this work, a new segmentation-driven hybrid framework for automated silicosis staging and detection from chest radiographs, called SilicoNet (Silicosis+Network), is proposed. Two separate experiments were conducted to validate the framework. In the first experiment, SilicoNet was used to segment lungs from pre-processed and augmented chest radiographs, and its performance was compared to that of the conventional U-Net model. In the second experiment, the outputs of the proposed custom Convolutional Neural Network (CNN) model were systematically compared with those of three popular CNN architectures used for classification: MobileNetV2, InceptionV2, and ResNet50. For validation, evaluation criteria such as the Dice similarity coefficient, Jaccard index, precision, recall, f1-score, accuracy, specificity, matthews correlation coefficient, negative predictive value, and training length were used. The findings show that SilicoNet and the customised CNN perform better than standard baselines, achieving a maximum accuracy of 96.40%. This study is distinctive because it combines improved segmentation and optimised classification, which results in better robustness and generalisability than previous models.

[1] Ahmad H., Milne M., Buchlak Q., Ektas N., and et al., “Machine Learning Augmented Interpretation of Chest X-Rays: A Systematic Review,” Diagnostics, vol. 13, no. 4, pp. 1-31, 2023. https://doi.org/10.3390/diagnostics13040743

[2] Aksoy H., Atila U., and Arslan S., “Deep Learning Based Detection of Silicosis from Computed Tomography Images,” Computer Methods and Programs in Biomedicine Update, vol. 6, pp. 1-18, 2024. https://doi.org/10.1016/j.cmpbup.2024.100166

[3] Ali O., Ali H., Shah S., and Shahzad A., “Implementation of a Modified U-Net for Medical Image Segmentation on Edge Devices,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 69, no. 11, pp. 4593-4597, 2022. https://doi.org/10.1109/TCSII.2022.3181132

[4] Alotaibi A., “Ensemble Deep Learning Approaches in Health Care: A Review,” Computers, Materials and Continua, vol. 82, no. 3, pp. 3741-3771, 2025. https://doi.org/10.32604/cmc.2025.061998

[5] Arvind S., Tembhurne J., Diwan T., and Sahare P., “Improvised Light Weight Deep CNN Based U- Net for the Semantic Segmentation of Lungs from Chest X-Rays,” Results in Engineering, vol. 17, pp. 100929, 2023. https://doi.org/10.1016/j.rineng.2023.100929

[6] Behera N., Gurunathan K., Kaliappan S., Rai V., and Manikandan V., “Enhanced CNN-Based Decision Support Framework for Gastrointestinal Neuroendocrine Tumors Using Inception-Resnet- V2,” in Proceedings of the IEEE International Conference on Big Data and Machine Learning, Bhopal, pp. 145-150, 2024. https://doi.org/10.1109/ICBDML60909.2024.105 77307

[7] Chen W., Yang F., Zhang X., Xu X., and Qiao X., “MAU-Net: Multiple Attention 3D U-Net for Lung Cancer Segmentation on CT Images,” Procedia Computer Science, vol. 192, pp. 543- 552, 2021. https://doi.org/10.1016/j.procs.2021.08.056

[8] Chhabra M. and Kumar R., “An Efficient ResNet- 50 Based Intelligent Deep Learning Model to Predict Pneumonia from Medical Images,” in Proceedings of the International Conference on Sustainable Computing and Data Communication Systems, Erode, pp. 1714-1721, 2022. https://doi.org/10.1109/ICSCDS53736.2022.9760 995

[9] Devnath L., Luo S., Summons P., and Wang D., “An Accurate Black Lung Detection Using Transfer Learning Based on Deep Neural Networks,” in Proceedings of the International Conference on Image and Vision Computing New Zealand, Dunedin, pp. 1-6, 2019. https://doi.org/10.1109/IVCNZ48456.2019.89609 61

[10] Devnath L., Luo S., Summons P., Wang D., and et al., “Deep Ensemble Learning for the Automatic Detection of Pneumoconiosis in Coal Worker’s Chest X-Ray Radiography,” Journal of Clinical Medicine, vol. 11, no. 18, pp. 1-23, 2022. https://doi.org/10.3390/jcm11185342

[11] Ehrlich R., Barker S., Naude J., Rees D., and et al., “Accuracy of Computer-Aided Detection of Occupational Lung Disease: Silicosis and Pulmonary Tuberculosis in Ex-Miners from the South African Gold Mines,” International Journal of Environmental Research and Public Health, A Silicosis-Focused Hybrid Architecture Leveraging Enhanced Segmentation and ... 295 vol. 19, no. 19, pp. 1-14, 2022. https://doi.org/10.3390/ijerph191912402

[12] Eravci D. and Erdebilli B., “Diagnosing Silicosis Using Adaptive Neuro-Fuzzy Inference System,” Research Square, vol. 1, pp. 1-18, 2022. https://doi.org/10.21203/rs.3.rs-450276/v1

[13] Gite S., Mishra A., and Kotecha K., “Enhanced Lung Image Segmentation Using Deep Learning,” Neural Computing and Applications, vol. 35, no. 31, pp. 22839-22853, 2023. https://doi.org/10.1007/s00521-021-06719-8

[14] Gunasekaran S. and Vivekasaran S., “Disease Prognosis of Fetal Heart’s Four-Chamber and Blood Vessels in Ultrasound Images Using CNN Incorporated VGG 16 and Enhanced DRNN,” The International Arab Journal of Information Technology, vol. 21, no. 6, pp. 1111-1127, 2024. https://doi.org/10.34028/iajit/21/6/13

[15] Hossain M., “Microc Alcification Segmentation Using Modified U-Net Segmentation Network from Mammogram Images,” Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 2, pp. 86-94, 2022. https://doi.org/10.1016/j.jksuci.2019.10.014

[16] Hussain S., Naz T., Shakeel M., Akram F., and et al., “Advanced Imaging Technique-based Brain Tumor Segmentation Using ResNET-50 CNN,” in Proceedings of the 5th International Conference on Sustainable Innovation in Engineering and Technology, Kuala Lumpur, pp. 20156, 2024. https://doi.org/10.1063/5.0229438

[17] Islam R., Rahman M., Ali S., Nafi A., and et al., “Enhancing Breast Cancer Segmentation and Classification: An Ensemble Deep Convolutional Neural Network and U-Net Approach on Ultrasound Images,” Machine Learning with Applications, vol. 16, pp. 100555, 2024. https://doi.org/10.1016/j.mlwa.2024.100555

[18] Koul A., Bawa R., and Kumar Y., “Artificial Intelligence Techniques to Predict the Airway Disorders Illness: A Systematic Review,” Archives of Computational Methods in Engineering, vol. 30, no. 2, pp. 831-864, 2023. https://doi.org/10.1007/s11831-022-09818-4

[19] Kumarasinghe K., Kolonne S., Fernando K., and Meedeniya D., “U-Net Based Chest X-Ray Segmentation with Ensemble Classification for Covid-19 and Pneumonia,” International Journal of Online and Biomedical Engineering, vol. 18, no. 7, pp. 161-174, 2022. https://doi.org/10.3991/ijoe.v18i07.30807

[20] Lozano M., Vera L., Reyes A., Arteaga D., and et al., “Deep Learning for Glaucoma Detection: R- Cnn Resnet-50 and Image Segmentation,” Journal of Advances in Information Technology, vol. 14, no. 6, pp. 1186-1197, 2023. DOI: 10.12720/jait.14.6.1186-1197

[21] Munawar F., Azmat S., Iqbal T., Gronlund C., and Ali H., “Segmentation of Lungs in Chest X-Ray Image Using Generative Adversarial Networks,” IEEE Access, vol. 8, pp. 153535-153545, 2020. https://doi.org/10.1109/ACCESS.2020.3017915

[22] Saifullah S., Yudhana A., and Suryotomo A., “Automatic Brain Tumor Segmentation: Advancing U-Net with ResNet50 Encoder for Precise Medical Image Analysis,” IEEE Access, vol. 13, pp. 43473-43489, 2025. https://doi.org/10.1109/ACCESS.2025.3547430

[23] Sharma G., Harjule P., Agarwal B., and Kumar R., “Silicosis Detection Using Extended Transfer Learning Model,” in Proceedings of the International Conference on Recent Trends in Image Processing and Pattern Recognition, Derby, pp. 111-126, 2023. https://doi.org/10.1007/978-3-031-53085-2_10

[24] Shivaanivarsha N. and Kavipriya P., “Identification of Silicosis using Deep Learning Model on Computed Tomography Images,” in Proceedings of the IEEE 3rd Mysore Sub Section International Conference, Hassan, pp. 1-6, 2023. https://doi.org/10.1109/MysuruCon59703.2023.1 0396879

[25] Shivaanivarsha N., Kavipriya P., and Vigita S., “A Deep Transfer Learning Approach for the Detection of Silicosis Using Chest Radiography,” in Proceedings of the National Conference on Control Instrumentation System Conference, vol. 1236, pp. 575-589, 2023. https://doi.org/10.1007/978-981-97-5866-1_41

[26] Singh R., Gupta S., Bharany S., Almogren A., and et al., “Ensemble Deep Learning Models for Enhanced Brain Tumor Classification by Leveraging ResNet50 and EfficientNet-B7 on High-Resolution MRI Images,” IEEE Access, vol. 12, pp. 178623-178641, 2024. https://doi.org/10.1109/ACCESS.2024.3494232

[27] Tarhini H., Mohamad R., Rammal A., and Ayache M., “Lung Segmentation Followed by Machine Learning and Deep Learning Techniques for COVID-19 Detection in Lung CT Images,” in Proceedings of the 6th International Conference on Advances in Biomedical Engineering, Werdanyeh, pp. 222-227, 2021. https://www.researchgate.net/publication/355166 634_Lung_Segmentation_followed_by_Machine _Learning_Deep_Learning_Techniques_for_CO VID-19_Detection_in_lung_CT_Images

[28] Xu W., Fu Y., and Zhu D., “ResNet and its Application to Medical Image Processing: Research Progress and Challenges,” Computer Methods and Programs in Biomedicine, vol. 240, pp. 107660, 2023. https://doi.org/10.1016/j.cmpb.2023.107660

[29] Yang X., Qu S., Wang Z., Li L., and et al., “The Study on Ultrasound Image Classification Using a Dual-Branch Model Based on Resnet50 Guided 296 The International Arab Journal of Information Technology, Vol. 23, No. 2, March 2026 by U-Net Segmentation Results,” BMC Medical Imaging, vol. 24, no. 1, pp. 1-16, 2024. https://doi.org/10.1186/s12880-024-01486-z

[30] Younis A., Li Q., Afzal Z., Adamu M., and et al., “Abnormal Brain Tumors Classification Using Resnet50 and its Comprehensive Evaluation,” IEEE Access, vol. 12, pp. 78843-78853, 2024. https://doi.org/10.1109/ACCESS.2024.3403902

[31] Yu L., Shaheema S., Sunil J., Govindan V., and et al., “Breast Cancer Segmentation Using a Hybrid AttendSeg Architecture Combined with a Gravitational Clustering Optimization Algorithm Using Mathematical Modelling,” Open Physics, vol. 21, no. 1, pp. 1-13, 2023. file:///C:/Users/acit2k/Downloads/10.1515_phys- 2023-0105.pdf

[32] Zaw K. and Mon A., “Enhanced Multi-Class Skin Lesion Classification of Dermoscopic Images Using an Ensemble of Deep Learning Models,” Journal of Computing Theories and Applications, vol. 2, no. 2, pp. 256-267, 2024. https://doi.org/10.62411/jcta.11530

[33] Zhang L., Rong R., Li Q., Yang D., and et al., “Deep Learning-Based Model for Screening and Staging Pneumoconiosis,” Scientific Reports, vol. 11, no. 1, pp. 1-7, 2021. https://doi.org/10.1038/s41598-020-77924-z

[34] Zhang Y., “Computer-Aided Diagnosis for Pneumoconiosis Staging Based on Multi-Scale Feature Mapping,” International Journal of Computational Intelligence Systems, vol. 14, no. 1, pp. 1-11, 2021. https://doi.org/10.1007/s44196- 021-00046-5

[35] Zhu L., Zheng R., Jin H., Zhang Q., and Zhang W., “Automatic Detection and Recognition of Silicosis in Chest Radiograph,” Bio-Medical Materials and Engineering, vol. 24, no. 6, pp. 3389-3395, 2014. https://doi.org/10.3233/bme- 141162