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Deep Hybrid Learning for Accurate Lung Nodule Classification in CT Scans
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 28 |
| Year of Publication: 2025 |
| Authors: Vishwas V. Patange, Sanjay L. Nalbalwar, Jagadish B. Jadhav, Suchitra Shankar Hyam |
10.5120/ijca2025925382
|
Vishwas V. Patange, Sanjay L. Nalbalwar, Jagadish B. Jadhav, Suchitra Shankar Hyam . Deep Hybrid Learning for Accurate Lung Nodule Classification in CT Scans. International Journal of Computer Applications. 187, 28 ( Aug 2025), 18-22. DOI=10.5120/ijca2025925382
Abstract
Lung cancer is one of the leading causes of cancer-related deaths worldwide, and early detection is critical for improving patient survival rates. This study presents an advanced CT scan–based image analysis pipeline for the reliable detection and classification of pulmonary nodules. The proposed method combines image preprocessing, segmentation, and feature extraction with deep learning classification to improve accuracy and robustness. We address challenges such as class imbalance, domain shift, and inter-class similarity—particularly between benign and normal cases—by applying targeted augmentation, class-balanced losses, and vessel-suppression techniques. Experimental evaluation on benchmark datasets shows that our approach achieves high accuracy and recall, especially for malignant cases, while minimizing false negatives in benign detection. The results highlight the potential of our method for integration into computer-aided diagnosis systems to support radiologists in clinical decision-making.
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