Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) Architecture for Lung Cancer Subtype Classification

Lung cancer is a leading cause of cancer-related deaths worldwide. Accurate classification of lung cancer subtypes is crucial for effective treatment. This study proposes a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture for classifying lung cancer subtypes: Adenocarcinoma, Squamous Cell Carcinoma, and Lymph Node involvement. Our model achieves 95.38% on a dataset LC105K of 105,000 images, outperforming traditional methods. The CNN-LSTM architecture extracts features from images and models temporal dependencies, improving classification accuracy.