A Visual-Modified Circular Dilated Convolutional Neural Network with Draco Lizard Optimizer Framework for Automated Lung Disease Detection Using CT Images
DOI:
https://doi.org/10.63949/Keywords:
- Lung Disease Classification,
- CT Images,
- Visual-Modified Circular Dilated Convolutional Neural Network,
- Draco Lizard Optimizer,
- Adaptive Self-Guided Loop Filter
Abstract
Lung Disease is one of the prominent causes of death, hence the need for its early diagnosis and accurate detection via CT images. Conventional approaches to diagnosis may have limitations in identifying intricate patterns in lung disorders; hence, the possibility of diagnosis may suffer, leading to delayed medical attention. To mitigate these limitations, the research proposes the Visual-Modified Circular Dilated Convolutional Neural Network with Draco Lizard Optimizer (V-CDCNNet-DLO) for efficient classification of lung disorders. CT images from the LIDC-IDRI dataset are first preprocessed using the Adaptive Self-Guided Loop Filter (AS-GLF) to enhance image quality. The research utilizes the V-CDCNNet for feature extraction and classification, with the help of the weights provided by the Draco Lizard Optimizer (DLO) to improve its performance. The research has demonstrated excellent performance with a high level of accuracy of 99.27%, precision of 99.20%, recall value of 99.15%, and error level of 0.73%. This model establishes a reliable framework for early lung disease detection.
Downloads
References
[1] Singh, A. P., Singh, A., Kumar, A., Agarwal, H., Yadav, S., & Gupta, M. (2024). Development of an artificial neural network-based image retrieval system for lung disease classification and identification. Engineering Proceedings, 62(1), 2.
[2] Wankhade, S., & Vigneshwari, S. (2023). A novel hybrid deep learning method for early detection of lung cancer using neural networks. Healthcare Analytics, 3, 100195.
[3] Karaddi, S. H., & Sharma, L. D. (2023). Automated multi-class classification of lung diseases from CXR-images using pre-trained convolutional neural networks. Expert Systems with Applications, 211, 118650.
[4] Rajasekar, V., Vaishnnave, M. P., Premkumar, S., Sarveshwaran, V., & Rangaraaj, V. (2023). Lung cancer disease prediction with CT scan and histopathological images feature analysis using deep learning techniques. Results in Engineering, 18, 101111.
[5] Azam, S., Rafid, A. R. H., Montaha, S., Karim, A., Jonkman, M., & De Boer, F. (2023). Automated detection of broncho-arterial pairs using CT scans employing different approaches to classify lung diseases. Biomedicines, 11(1), 133.
[6] Hussain Ali, Y., Sabu Chooralil, V., Balasubramanian, K., Manyam, R. R., Kidambi Raju, S., Sadiq, A. T., & Farhan, A. K. (2023). Optimization system based on convolutional neural network and internet of medical things for early diagnosis of lung cancer. Bioengineering, 10(3), 320.
[7] Podder, P., Das, S. R., Mondal, M. R. H., Bharati, S., Maliha, A., Hasan, M. J., & Piltan, F. (2023). LDDNet: A deep learning framework for the diagnosis of infectious lung diseases. Sensors, 23(1), 480.
[8] AR, B., RS, V. K., & SS, K. (2023). LCD-capsule network for the detection and classification of lung cancer on computed tomography images. Multimedia Tools and Applications, 82(24), 37573–37592.
[9] Bushara, A. R., Kumar, R. V., & Kumar, S. S. (2023). An ensemble method for the detection and classification of lung cancer using computed tomography images utilizing a capsule network with visual geometry group. Biomedical Signal Processing and Control, 85, 104930.
[10] Gautam, N., Basu, A., & Sarkar, R. (2024). Lung cancer detection from thoracic CT scans using an ensemble of deep learning models. Neural Computing and Applications, 36(5), 2459–2477.
[11] Gupta, A., Kumar, A., & Rautela, K. (2025). UDCT: Lung cancer detection and classification using U-Net and DARTS for medical CT images. Multimedia Tools and Applications, 84(18), 19065–19085.
[12] Yin, W., Zhang, K., Zhang, L., Wang, Y., & Liu, H. (2021). Adaptive self-guided loop filter for video coding. In Proceedings of the 2021 International Conference on Visual Communications and Image Processing (VCIP) (pp. 1–5). IEEE.
[13] Song, X., Han, D., Chen, C., Shen, X., & Wu, H. (2025). VMAN: Visual-modified attention network for multimodal paradigms. The Visual Computer, 41(4), 2737–2754.
[14] Cheng, L., Khalitov, R., Yu, T., Zhang, J., & Yang, Z. (2023). Classification of long sequential data using circular dilated convolutional neural networks. Neurocomputing, 518, 50–59.
[15] Wang, X. (2025). Draco lizard optimizer: A novel metaheuristic algorithm for global optimization problems. Evolutionary Intelligence, 18(1), 10.
[16] LIDC-IDRI dataset. (n.d.). Kaggle. https://www.kaggle.com/datasets/zhangweiled/lidcidri
