International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Lung cancer remains a leading cause of cancer-related mortality, where early diagnosis is critical for improving survival outcomes. Computed Tomography (CT) imaging is commonly used for lung cancer screening; however, manual interpretation of CT scans is time-consuming and susceptible to diagnostic variability. This paper presents LUNG VISION, an automated lung cancer detection and classification system based on machine learning and deep learning techniques. The proposed framework includes image preprocessing, lung region segmentation, feature extraction, and classification. Preprocessing techniques such as resizing, normalization, and noise reduction are applied to enhance CT image quality. Machine learning classifiers including Decision Tree, Random Forest, and Gaussian Naive Bayes are implemented using Histogram of Oriented Gradients features. In parallel, deep learning models such as Convolutional Neural Networks, DenseNet, and ResNet are employed through transfer learning to automatically learn discriminative features from CT images. The system classifies CT scans into normal, benign, and malignant categories and provides severity-related insights to support clinical decision-making. Experimental results indicate that deep learning models achieve superior diagnostic accuracy and robustness compared to traditional machine learning methods. The system is deployed via a web-based interface to assist radiologists in early and reliable lung cancer diagnosis.

Keywords: Lung Cancer Detection, Computed Tomography (CT), Deep Learning, Convolutional Neural Network (CNN), Machine Learning.

Downloads: | DOI: 10.17148/IJARCCE.2026.15183