AI-Powered Lung Cancer Classification Using Hybrid Histopathological Image Analysis

Abstract

Lung cancer is a major cause of cancer-related mortality, and accurate classification of histopathological tissue patterns can support the analysis of lung cancer subtypes. Manual examination of histopathological images is time-consuming and requires careful assessment of cellular and tissue-level morphological patterns. To assist automated histopathological image analysis, this study presents a hybrid deep learning and machine learning framework for three-class classification of lung histopathological images. The proposed system employs an ImageNet-pretrained DenseNet201 convolutional neural network for deep feature extraction through transfer learning, followed by frozen-base training and fine-tuning of selected network layers. A learned 256-dimensional feature representation is subsequently extracted from the trained network and provided to a CatBoost classifier for final classification. The system categorizes images into lung adenocarcinoma, lung squamous cell carcinoma, and Benign Lung Tissue. On a validation set of 3,000 images, the proposed hybrid framework achieved an overall classification accuracy of 99.90%, with 2,997 images correctly classified. The framework combines deep convolutional feature learning with gradient-boosted machine learning classification to provide an automated and modular approach for lung histopathological image classification.