Mukesh Kumar; Vivek Bhardwaj; Kavita Dhiman; Ahmed Qtaishata

Deep Learning Method for Early Detection of Lung Cancer using Deep Learning Algorithms

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Author(s)

Mukesh Kumar ^1,2,* Vivek Bhardwaj ³ Kavita Dhiman ⁴ Ahmed Qtaishata ⁵

1. Advanced Centre of Research & Innovation (ACRI), School of Advance Computing, CGC University Mohali-140307, Punjab, India.

2. Faculty of Law, Sohar University Sohar, Sultanate of Oman.

3. Department of Computer Science & Engineering, Amity University Punjab, India

4. School of Advance Computing, CGC University Mohali-140307, Punjab, India.

5. General Foundation Program, Department of Information Technology, Sohar University Sohar, Sultanate of Oman

* Corresponding author.

DOI: https://doi.org/10.5815/ijem.2026.02.11

Received: 24 Jul. 2025 / Revised: 14 Nov. 2025 / Accepted: 14 Mar. 2026 / Published: 8 Apr. 2026

Index Terms

Lung Cancer Detection, Computed Tomography, Deep Learning, Convolutional Neural Networks, Hybrid Model, Medical Imaging

Abstract

Lung cancer is responsible for many deaths from cancer around the globe, primarily because it is difficult to find malignant lung nodules early enough to be treatable. We developed a hybrid deep learning approach to the automated classification of lung nodules from chest computed tomography (CT) images. Our model uses convolutional neural networks (CNNs) for hierarchical feature extraction, an attention mechanism for feature refinement in targeted regions of interest, and a support vector machine (SVM) classifier for robust margin-based decision making. Furthermore, we use a patch-based learning strategy within the model to improve sensitivity to small and ambiguous lung nodules. The model is tested on the publicly available LIDC-IDRI dataset and achieves 94.2% accuracy, 95.1% recall, and an area under the receiver operating characteristic curve (AUC-ROC) score of 0.971, which outperforms multiple baseline deep learning methods. The proposed method provides a synergistic integration of attention-weighted feature enhancements and traditional machine learning classifications as compared to traditional end-to-end architectures, resulting in improved model generalization and interpretability. Grad-CAM visualizations are also used to provide qualitative insights into the model decision-making process. The proposed hybrid approach provides a novel and interpretable solution for the classification of lung nodules from CT images that may assist in the development of computerized systems to assist physicians in making diagnoses using medical images.

Cite This Paper

Mukesh Kumar, Vivek Bhardwaj, Kavita Dhiman, Ahmed Qtaishata, "Deep Learning Method for Early Detection of Lung Cancer using Deep Learning Algorithms", International Journal of Engineering and Manufacturing (IJEM), Vol.16, No.2, pp.171-184, 2026. DOI:10.5815/ijem.2026.02.11

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