Lung Cancer Management: Revolutionizing Patient Outcomes Through Machine Learning and Artificial Intelligence.

ABSTRACT:

Background and aims

Lung cancer remains a leading cause of cancer-related deaths worldwide, with early detection critical for improving prognosis. Traditional machine learning (ML) models have shown limited generalizability in clinical settings. This study proposes a deep learning-based approach using transfer learning to accurately segment lung tumor regions from CT scans and classify images as cancerous or noncancerous, aiming to overcome the limitations of conventional ML models.

Methods

We developed a two-stage model utilizing a ResNet50 backbone within a U-Net architecture for lesion segmentation, followed by a multi-layer perceptron (MLP) for binary classification. The model was trained on publicly available CT scan datasets and evaluated on an independent clinical dat

SUBMITTER: Riahi T 

PROVIDER: S-EPMC12269930 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature