A Real-Time Application for Early-Stage Prediction of Lung Cancer and Its Severity Using Machine Learning

Abstract

Lung cancer remains one of the leading causes of cancer deaths around the world. It is known that the chances of a patient’s survival dramatically improve with early diagnosis; however, such diagnosis is often inaccurate with traditional diagnostic methods. The contribution of this study is to propose a machine learning based framework for early diagnosis of lung cancer utilizing advanced algorithms including Convolutional Neural Networks (CNNs), ResNet50 and efficient feature extraction techniques. Our model achieved 98 percent accuracy and 97 percent F1 score, outperforming many state-of-the-art techniques using multimodal imaging datasets. Future studies will focus on hybrid models which incorporate nanotechnology to enhance non-invasive diagnostics, developing solutions for scalability challenges.

Introduction

Summary:

Lung cancer remains the leading cause of cancer-related deaths worldwide, with early detection crucial for improving survival rates. Conventional diagnostic methods like CT scans and biopsies are expensive, invasive, and prone to errors, often leading to delayed treatment. This study explores the use of artificial intelligence, particularly deep learning and convolutional neural networks (CNNs), to develop a cost-effective, accurate, and efficient early lung cancer detection model by integrating advanced data preprocessing, feature extraction (e.g., DOST, histogram equalization), and hybrid machine learning architectures.

Despite promising advances in machine learning for lung cancer diagnosis—such as CNNs, ResNet50, GoogLeNet, and multimodal imaging—the challenges include data imbalance, computational demands, overfitting, and clinical applicability. The study addresses these by augmenting datasets, combining imaging with biomarker data, and improving interpretability through techniques like Grad-CAM.

Limitations include dependency on high-quality data, need for extensive clinical validation, regulatory hurdles, and ethical concerns regarding privacy and bias. Future directions suggest integrating multimodal datasets, enhancing model explainability, optimizing computational efficiency, and combining AI with emerging biomarker technologies for non-invasive detection. Ultimately, this research aims to create scalable, interpretable, and accessible AI tools to improve early lung cancer diagnosis and patient outcomes globally.

Conclusion

Here in this study, an overarching frame work is presented for machine-learning-based early-stage lung-cancer detection. Advanced CNN architectures such as ResNet50 and DOST, merged feature extraction with histogram equalization, yield a remarkable achievement from the proposed system in aspects like its accurate and reliable diagnosis. Coupling data augmentation with prepro-cessing addresses significant challenges of dataset imbalances and overfitting leading to making the model a better fit for real-life applications. The framework has a host of advantages over traditional diagnosis methods, including scalability and efficiency and potential real-time deployability. On the other hand, it also identifies crucial areas of future research, namely external validation on diverse datasets, model interpretability, and multimodal data sources. Future advancements in nanotechnology, computational efficiency, and the ethical implementation of AI would be crucial to expanding the scope of machine learning in healthcare. This multidisciplinary approach may revolutionize early lung cancer detection, reduce mortality rates, and enhance patient outcomes across the world.

References

[1] Tasnim N., Noor K.R., Islam M., Huda M.N., Sarker I.H. A deep learning-based image processing technique for early lung cancer prediction. Proceedings of the 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), pp. 1060-1063, 2024. [2] Kesav O.H., Rajini G.K. A systematic study on enhanced deep learning-based methodologies for detection and classification of early-stage cancers. Proceedings of the 2023 IEEE 5th International Conference on Cybernetics, Cognition, and Machine Learning Applications (ICCCMLA), pp. 328333, 2023. [3] Abdullah M.F., Karim N.K.A., Sulaiman S.N., Shuaib I.L., Osman M.K., Alhamdu M.D.I. Classification of lung cancer stages from CT scan images using image processing and knearest neighbours. Proceedings of the 2020 IEEE Control and System Graduate Research Colloquium (ICSGRC), pp. 68-71, 2020. [4] Romaszko A.M., Doboszynska A. Multiple primary´ lung cancer: A literature review. Advances in Clinical and Experimental Medicine, 27(5), pp. 725-730, 2018. [5] Faisal M.I., Bashir S., Khan Z.S., Khan F.H. An evaluation of machine learning classifiers and ensembles for early-stage prediction of lung cancer. Proceedings of the 2018 International Conference on Emerging Trends in Engineering, Sciences, and Technology (ICEEST), pp. 1-4, 2018. [6] Patra R. Prediction of lung cancer using machine learning classifiers. Computing Science, Communication, and Security: First International Conference (COMS2), pp. 132142, 2020. [7] Wang X., Peng Y., Lu L., Lu Z., Bagheri M., Summers R.M. ChestX-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2097-2106, 2017. [8] Chaturvedi P., Jhamb A., Vanani M., Nemade V. Prediction and classification of lung cancer using machine learning techniques. IOP Conference Series: Materials Science and Engineering, Vol. 1099, No. 1, p. 012059, 2021. [9] Pawar V.J., Kharat K.D., Pardeshi S.R., Pathak P.D. Lung cancer detection system using image processing and machine learning techniques. Cancer Journal, 3(2020), p. 4, 2020.

Copyright

Copyright © 2025 Madan Kumar R, Manoj S, Hemanth L M, Manish A R, Dr. Gowrishankar B S. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.