Paper Contents
Abstract
Lung cancer is among the most severe health threats worldwide, with patient survival rates highly dependent on accurate and timely stage. classification. Conventional diagnostic methods often rely on radiologistsexpertise, which can be time-intensive and prone to inter-observer variability. Recent advancements in artificial intelligence, specifically machine. learning (ML) and deep learning (DL), have brought up new opportunities for medical image analysis and decision support.In this work, we proposed a hybrid ML-DL framework for lung. cancer stage categorization using CT scan images. The approach integrates convolutional neural networks (CNN) for automated feature extraction and machine learning classifiers such as random forest and K-Nearest Neighbors (KNN) for robust decision-making. Additionally, a VGG16-based deep model was evaluated for comparative analysis.
Copyright
Copyright © 2025 Divya A M . This is an open access article distributed under the Creative Commons Attribution License.
