A Comparative Deep Learning Approach: CNN And Hybrid Algorithms For Lung Cancer Classification In Histopathology Images

The study designs a novel hybrid deep learning model (CNN-VGG16-ViT) that simultaneously resolves local tissue morphology extraction challenges and global multi-scale visual dependencies in histopathology samples. Evaluated against a highly balanced multi-class dataset of 15,000 images, this integrated local-global feature representation outperforms standard custom CNN baselines by boosting classification accuracy from 96.03% to 98.47% while yielding a robust micro-average AUC of 0.9913.