Authors : Ch. Jyothi; Vanguru Harikrishna; Nallabothula Srivani; Suruguru Sukhendhar; Mungi Harish

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/4tfzreys

Scribd : https://tinyurl.com/4m3rn6xr

DOI : https://doi.org/10.38124/ijisrt/26apr106

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Lung cancer is a leading cause of cancer-related mortality worldwide, emphasizing the need for accurate and reliable Computer-Aided Diagnosis (CAD) systems. This study presents an enhanced CAD framework for lung cancer detection using CT images with unmarked nodules by integrating machine learning and deep learning techniques. A Support Vector Machine (SVM) is used to classify nodules as benign or malignant based on 2D features such as texture, shape, and intensity, while a 3D Convolutional Neural Network (3D CNN) analyzes volumetric CT data to capture spatial tumor characteristics and improve tumor assessment. To enhance clinical transparency, an Explainable AI (XAI) module highlights critical regions influencing predictions. The framework also includes cancer stage estimation and tumor burden analysis to assess disease severity based on nodule size, number, and distribution. Additionally, an automated report generation system provides structured clinical outputs. Experimental results demonstrate improved accuracy, interpretability, and decision support, making the framework a reliable and practical solution for lung cancer detection and analysis.

Keywords : Lung Cancer Detection, Computed Tomography (CT), 3D Convolutional Neural Network (3D CNN), Support Vector Machine (SVM), Explainable Artificial Intelligence (XAI).

References :

1. Qiu S., Guo Q., Zhou D., Jin Y., Zhou T., He Z. (2019) Isolated pulmonary nodules characteristics detection based on CT images. IEEE Access.
2. Chen W., Zhou P., Dong S., Gong S., Hu M., Wang K., Wu D. (2018) Tree-based contextual learning for online job or candidate recommendation with big data support in professional social networks. IEEE Access.
3. Malarvannan S., Angulakshmi M. (2025) A review on lung cancer classification using deep learning techniques. IEEE Access.
4. Ayad S., Al-Jamimi H.A., El Kheir A. (2025) Integrating advanced techniques: RFE-SVM feature engineering and Nelder-Mead optimized XGBoost for accurate lung cancer prediction. IEEE Access.
5. Zuo W., Zhou F., Li Z., Wang L. (2019) Multi-resolution CNN and knowledge transfer for candidate classification in lung nodule detection. IEEE Access.
6. Monkam P., Qi S., Ma H., Gao W., Yao Y., Qian W. (2019) Detection and classification of pulmonary nodules using convolutional neural networks: A survey. IEEE Access.
7. Nguyen C.C., Tran G.S., Nguyen V.T., Burie J.C., Nghiem T.P. (2021) Pulmonary nodule detection based on Faster R-CNN with adaptive anchor box. IEEE Access.
8. Amin M.M., Ismail A.S., Shaheen M.E. (2024) Multimodal non-small cell lung cancer classification using convolutional neural networks. IEEE Access.

9. Saha C., Saha S., Rahman M.A., Milu M.M.H., Higa H., Rashid M.A., Ahmed N. (2025) Lung-AttNet: An attention mechanism-based CNN architecture for lung cancer detection with federated learning. IEEE Access.
10. Haque F., Hasan M.A., Siddique M.A.I., Roy T., Shaha T.K., Islam Y., Paul A., Chowdhury M.E.H. (2025) An end-to-end concatenated CNN attention model for the classification of lung cancer with XAI techniques. IEEE Access.
11. Yu H., Zhou Z., Wang Q. (2020)
    Deep learning assisted predict of lung cancer on computed tomography images using the adaptive hierarchical heuristic mathematical model. IEEE Access.
12. Harale A.M., Bairagi V.K., Boonchieng E., Bachute M.R. (2024)
    Nodules detection in lungs CT images using improved YOLOv5 and classification of types of nodules by CNN-SVM. IEEE Access.
13. Noaman N.F., Kanber B.M., Al Smadi A., Jiao L., Alsmadi M.K. (2024) Advancing oncology diagnostics: AI-enabled early detection of lung cancer through hybrid histological image analysis. IEEE Access.
14. Ye Y., Tian M., Liu Q., Tai H.M. (2020) Pulmonary nodule detection using V-Net and high-level descriptor based SVM classifier. IEEE Access.
15. Naseer I., Akram S., Masood T., Rashid M., Jaffar A. (2023)
    Lung cancer classification using modified U-Net based lobe segmentation and nodule detection. IEEE Access.