Lung Cancer Detection Using Deep Learning

Abstract

Lung cancer remains a significant global health challenge, with early and accurate detection being crucial for improving survival rates. Current diagnostic methods, includ-ing Computer-Aided Diagnosis (CAD) systems, often rely on semi-automatic processes that require substantial input from radiologists, leading to variability and delays in diagnosis. This paper introduces an End-to-End Fully Automated Lung Cancer Screening System designed to address these challenges. The pro- posed system integrates five key modules: abnormality detection, cancer segmentation, volume estimation, cancer grading, and an earlywarningsystem.Anovelmodifiedconvolutiontechnique is employed to enhance boundary retention and accuracy in segmentation,achievingasegmentationaccuracyof92.09%.The volume estimation model utilizes Gaussian Process Regression (GPR), improving accuracy to 94.18%, while the grading model follows the TNM classification, reaching an accuracy of 96.4%. Theearlywarningmoduleprovidesreal-timealertsforchangesin patient conditions, facilitating timely interventions. This holistic approach aims to streamline lung cancer screening, reduce diagnostic delays, and improve patient outcomes.

Introduction

Lung cancer is a leading cause of cancer deaths worldwide, with early detection critical for better treatment outcomes. Traditional diagnosis via manual CT scan interpretation is laborious and subjective, causing delays and inconsistencies. Computer-aided diagnosis (CAD) systems offer automation but face limitations, especially in accurately segmenting complex lung nodules.

This study proposes a fully automated lung cancer screening system that integrates multiple diagnostic tasks—from anomaly detection and segmentation to volume estimation, cancer grading, and continuous monitoring. Key innovations include a modified convolution technique for precise nodule boundary detection, Gaussian Process Regression for volume estimation, and an early warning module for real-time alerts. The system is designed to be user-friendly, maintain clinical standards, ensure data security, and fit seamlessly into clinical workflows.

The literature review highlights advances in deep learning, segmentation, radiomics, and classification methods, showing improvements in detection accuracy and prognosis prediction. Experimental results demonstrate the system’s effectiveness in accurately detecting and classifying lung cancer types, reducing false positives, and enhancing diagnostic efficiency.

Overall, this integrated automated system promises to improve early lung cancer diagnosis, streamline clinical workflows, and potentially reduce mortality.

Conclusion

The End-to-End Fully Automated Lung Cancer Screening System introduced in this research addresses critical chal- lenges in lung cancer diagnostics by integrating advanced modules for abnormality detection, precise segmentation, ac- curatevolumeestimation,detailedgrading,andreal-timemon- itoring. By leveraging innovative techniques such as modified convolution and Gaussian Process Regression, the system achieveshighaccuracyacrossalldiagnostictasks,significantly enhancing the efficiency and reliability of lung cancer screen- ing. This holistic approach not only reduces the workload on radiologistsbutalsofacilitatestimelyinterventions,potentially improving patient outcomes and reducing mortality rates. Fu- ture work should focus on expanding the dataset and refining predictive capabilities to further enhance the system’s impact on clinical practice.

References

[1] Mohammaf A.Alzubaidi, Hamza jardat. “Self-Upgraded Cat MouseOptimizer With Machine Learning Driven Lung Cancer ClassificationonComputedTomographyImaging”ComputingandInformationTech-nology volume 11, 2023. [2] Mohammad A. Alzubaidi (maalzubaidi@yu.edu.jo)“Comprehensiveand Comparative Global and Local Feature Extraction Framework forLung Cancer Detection Using CT Scan Images,” VOLUME 9, 2021 . [3] Rabbia Mahum (rabbia.mahum@uettaxila.edu.pk) and Abdulmalik S.Al-Salman“Lung-RetinaNet:LungCancerDetectionUsingaRetinaNetWithMulti-ScaleFeatureFusionandContextModule:Asurvey,”IEEETrans. Med. Imag., volume- 11, 2023. [4] Pushkar Sathe (pushkarmsathe@gmail.com) “End-to-End Fully Auto-mated Lung Cancer Screening System ” 108515, VOLUME 12, 2024 [5] A.El-Baz,G.Gimel’farb,R.Falk,andM.AboEl-Ghar,“AnewCAD system for early diagnosis of detected lung nodules,” in Proc.IEEE Int. Conf. Image Process., vol. 2, Oct. 2007, pp. 461–464, doi:10.1109/ICIP.2007.4379192. [6] P.B.Sangamithraa,S.Govindaraju,“LungTumourDetectionandClassi-fication using EK-Mean Clustering”, International Journal of EmergingTechnology and Advanced Engineering, Volume 4, Issue 7, July 2020.. [7] MahmoudragabFatmahYousefAssiri,iyadkatib,(member,ieeeSelf-Upgraded Cat Mouse Optimizer With Machine Learning DrivenLung Cancer Classification on Computed Tomography ImagingA newoptimized sequential method for lung tumor diagnosis based on deeplearning and converged search and rescue algorithm,” Biomed. SignalProcess. Control, vol. 68, Oct. 2023, Art. no. 102761. [8] MuhammadMuzammil,(m.muzammil@iiu.edu.pk)DeepFeatureSelec-tion and Decision Level Fusion for Lungs Nodule Classification, Med.Image Anal., vol. 57, pp. 237248, Feb.2021. [9] Shatnawi, M. Q., Abuein, Q.,Al-Quraan, R. (2024). Deep learning-based approach to diagnose lung cancer using CT-scan images.Intelligence-Based Medicine, 11, 100188. [10] K. M. A. Alheeti, T. T. Al-Shouka, S. H. Majeed, and A. A. Ahmed,”Lung Cancer Detection Using Machine Learning and Deep LearningModels,” in 2024 21st International Multi-Conference on Systems,SignalsDevices(SSD),2024.DOI:10.1109/SSD61670.2024.10549507.

Copyright

Copyright © 2025 Dr. Vinod Wadne, Shubham Madane, Karan Kolhatkar, Vivek Pawar. 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.