A Machine Learning Framework for Early Detection and Prognostic Assessment of Lung Cancer Using CT Imaging

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Mr. Tokeshwar Prasad; Dr. Ranu Pandey

A Machine Learning Framework for Early Detection and Prognostic Assessment of Lung Cancer Using CT Imaging

Authors: Mr. Tokeshwar Prasad, Dr. Ranu Pandey

Unique Paper ID: 183906
Volume: 12
Issue: 3
PageNo: 3272-3278

Keywords: Lung Cancer Detection Computed Tomography (CT) Imaging Machine Learning Image Processing and Genetic Algorithm.

Abstract:
Lung cancer remains a leading cause of cancer-related mortality worldwide, necessitating advanced methods for early detection and prognosis to improve patient outcomes; this study proposes an integrated machine learning system that leverages Computed Tomography (CT) scans for lung nodule detection and predicts postoperative survival rates through a multi-stage analytical approach. The system begins with advanced image preprocessing and segmentation techniques to isolate lung nodules, followed by feature extraction optimized using a Genetic Algorithm (GA) to enhance discriminative power, and employ a Convolutional Neural Network (CNN) for accurate malignancy classification. Additionally, the framework incorporates a prognostic module utilizing a Multi-Layer Perceptron (MLP) trained on postoperative clinical data—including histopathological, demographic, and treatment-related variables—to predict patient survival likelihood, thereby enabling personalized treatment planning. Experimental validation on thoracic oncology datasets demonstrates the system's effectiveness in both diagnostic accuracy and predictive performance, offering clinicians a reliable decision-support tool that bridges automated image analysis with data-driven prognostic insights. By combining early detection capabilities with survival prediction, this approach addresses critical gaps in lung cancer management, reducing diagnostic subjectivity and facilitating timely interventions while highlighting the transformative potential of artificial intelligence in oncology.

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Cite This Article

ISSN: 2349-6002
Volume: 12
Issue: 3
PageNo: 3272-3278

A Machine Learning Framework for Early Detection and Prognostic Assessment of Lung Cancer Using CT Imaging

Available:https://ijirt.org/article?manuscript=183906

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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