Abstract :

Lung Cancer Remains One Of The Leading Causes Of Cancer-related Deaths Worldwide, Primarily Due To Late-stage Diagnosis And The Lack Of Effective Early Screening Methods. Early Detection Significantly Improves Treatment Outcomes And Patient Survival Rates. Traditional Diagnostic Techniques Such As Computed Tomography (CT), Biopsy, And Imaging-based Examinations, While Effective, Can Be Expensive, Invasive, And Time-consuming. Recent Advancements In Artificial Intelligence (AI) And Breath Analysis Technologies Have Introduced Promising Non-invasive Approaches For Early Lung Cancer Detection. This Paper Presents An AI-enhanced Breath Analysis Framework For Predicting Lung Cancer By Analyzing Volatile Organic Compounds (VOCs) Present In Exhaled Breath. The Proposed System Utilizes Advanced Sensor Technologies To Capture Breath Samples And Employs Machine Learning And Deep Learning Algorithms To Identify Patterns Associated With Lung Cancer Biomarkers. Data Preprocessing, Feature Extraction, And Predictive Modeling Techniques Are Integrated To Improve Diagnostic Accuracy And Reliability. Experimental Analysis Demonstrates That AI-driven Breath Analysis Provides Rapid, Non-invasive, And Costeffective Lung Cancer Prediction With High Sensitivity And Specificity. The Proposed Framework Offers An Intelligent Solution For Early Screening, Clinical Decision Support, And Improved Healthcare Outcomes

Published:

26-9-2026

Issue:

Vol. 26 No. 9 (2026)

Page Nos:

412-420

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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