Early Lung Cancer Detection via AI-Enhanced CT Image Processing Software

TL;DR

This paper presents an AI system that improves early lung cancer detection by analyzing CT scans, achieving over 90% accuracy in identifying cancerous nodules.

Contribution

The novel contribution is an AI-enhanced CT image processing system using an ensemble of machine learning algorithms for improved diagnostic accuracy.

Findings

The AI system achieved classification accuracy exceeding 90% in identifying malignant and non-malignant lung nodules.

The ensemble model outperformed individual classifiers in diagnostic accuracy and robustness.

The system enhances image visualization and preprocessing, supporting clinical decision-making in lung cancer screening.

Abstract

Background/Objectives: Lung cancer remains the leading cause of cancer-related mortality worldwide among both men and women. Early and accurate detection is essential to improve patient outcomes. This study explores the use of artificial intelligence (AI)-based software for the diagnosis of lung cancer through the analysis of medical images in DICOM format, aiming to enhance image visualization, preprocessing, and diagnostic precision in chest computed tomography (CT) scans. Methods: The proposed system processes DICOM medical images converted to standard formats (JPG or PNG) for preprocessing and analysis. An ensemble of classical machine learning algorithms—including Random Forest, Gradient Boosting, Support Vector Machine, and K-Nearest Neighbors—was implemented to classify pulmonary images and predict the likelihood of malignancy. Image normalization, denoising, segmentation, and feature extraction were performed to improve model reliability and reproducibility. Results: The AI-enhanced system demonstrated substantial improvements in diagnostic accuracy and robustness compared with individual classifiers. The ensemble model achieved a classification accuracy exceeding 90%, highlighting its effectiveness in identifying malignant and non-malignant lung nodules. Conclusions: The findings indicate that AI-assisted CT image processing can significantly contribute to the early detection of lung cancer. The proposed methodology enhances diagnostic confidence, supports clinical decision-making, and represents a viable step toward integrating AI into radiological workflows for early cancer screening.

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