International Journal on Science and Technology

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Lung Cancer Detection Using Deep learning

Author(s) Anant Gaur, Gunjan Sharma, Ayush Tyagi, Azad Rana
Country India
Abstract Lung cancer remains a significant contributor to global cancer mortality, where timely detection is crucial for effective treatment and improved survival rates. Despite the promise of imaging technologies and deep learning (DL)-based diagnostics, their deployment in resource-limited healthcare environments faces challenges due to computational complexity and data imbalance. This study introduces a lightweight, DL-driven approach for lung cancer detection that integrates PET and CT imaging. The proposed system enhances diagnostic reliability by incorporating image normalization, correction techniques, and data augmentation using generative adversarial networks (GANs). The model architecture includes DenseNet-121 for extracting hierarchical features, deep autoencoders for reducing dimensionality, and MobileNet V3-Small for fast classification. To optimize computational efficiency without compromising performance, strategies like quantization-aware training and early stopping were implemented. When tested on the Lung-PET-CT-Dx dataset (comprising over 31,000 labelled images), the model achieved an accuracy of 98.6% and a Cohen’s Kappa score of 95.8. These results suggest strong potential for clinical application in early lung cancer screening, particularly in settings with limited infrastructure. Further research will explore adaptive models such as liquid neural networks and ensemble methods to expand usability across broader medical domains.
Keywords Keywords - Lung Cancer, Deep Learning, Generative Adversarial Networks (GANs), Data Augmentation, Quantization-Aware Training (QAT), Automated Diagnosis, Fusion Imaging, Lightweight Models.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 16, Issue 2, April-June 2025
Published On 2025-05-11
DOI https://doi.org/10.71097/IJSAT.v16.i2.4854
Short DOI https://doi.org/g9kc63
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