Dual-Path Multi-Scale CNN for Precise Classification of Non-Small Cell Lung Cancer

Abstract

Non-Small Cell Lung Cancer (NSCLC) has the highest cancer-related mortality rate worldwide. While biopsy-based diagnosis is critical for prognosis and treatment, the intricate anatomical features in Whole Slide Images (WSIs) make manual classification challenging for pathologists. Current deep learning models have been developed to aid in the automatic classification of NSCLC, but many rely on extensive manual annotations and lack efficient multi-scale feature extraction, limiting their ability to capture diverse patterns in WSIs. There is a need to explore multipath, multi-scale Convolutional Neural Networks (CNN) that can effectively capture these diverse patterns in WSIs. This study proposes a novel deep learning model, a Multi-scale, Dual-Path CNN (MDP-CNN), designed to automatically classify NSCLC subtypes by capturing heterogeneous patterns and features in WSIs. The model was trained on two independent datasets, LC25000 and The Cancer Genome Atlas (TCGA), demonstrating notable improvements in performance metrics, achieving accuracy scores of 0.981 and 0.958, Area Under Curve (AUC) scores of 0.978 and 0.995, and kappa scores of 0.957 and 0.903 for the LC25000 and TCGA datasets, respectively. Extensive analyses, including ablation studies, interpretation plots, and cross-dataset analysis, were conducted to demonstrate the efficacy of the proposed model. Multi-scale processing improved the model's precision in classifying lung cancer subtypes by capturing variations in histopathological features across different resolutions. The proposed model outperformed state-of-the-art models by approximately 8% in accuracy and 3% in AUC, demonstrating the effectiveness of MDP CNNs in improving WSI-based diagnostics and supporting automated NSCLC classification and clinical decisions.