Recent Papers

Lung cancer remains one of the most prevalent and deadly diseases worldwide, necessitating advancements in early detection and classification techniques. Recent advancements in artificial intelligence (AI) and machine learning (ML) have led to significant improvements in automated lung cancer classification. This study presents a hybrid deep learning and machine learning approach utilizing MobileNetV2 for feature extraction, t-SNE for dimensionality reduction, and a Voting Classifier composed of Random Forest, Support Vector Machine (SVM), and LightGBM for classification. The dataset consists of microscopic images categorized into three types of lung cancer: adenocarcinoma, squamous cell carcinoma, and neuroendocrine tumors. The proposed Voting Classifier model achieved an accuracy of 96.4%, outperforming traditional single classifiers. By leveraging the collective decision-making power of multiple classifiers, our approach enhances prediction reliability and robustness. The results demonstrate that ensemble learning can significantly improve lung cancer classification, paving the way for its potential integration into real-world diagnostic systems.

Copyright © 2025 Atharva Gadhave. This is an open access article distributed under the Creative Commons Attribution License.