Performance analysis of various deep learning models based on Max-Min CNN for lung nodule classification on CT images

Abstract

Lung cancer remains one of the leading causes of cancer-related deaths worldwide, underlining the urgent need for accurate and early detection and classification methods. In this paper, we present a comprehensive study that evaluates and compares different deep learning techniques for accurately distinguishing between nodule and non-nodule in 2D CT images. Our work introduced an innovative deep learning strategy called “Max-Min CNN” to improve lung nodule classification. Three models have been developed based on the Max-Min strategy: (1) a Max-Min CNN model built and trained from scratch, (2) a Bilinear Max-Min CNN composed of two Max-Min CNN streams whose outputs were bilinearly pooled by a Kronecker product, and (3) a hybrid Max-Min ViT combining a ViT model built from scratch and the proposed Max-Min CNN architecture as a backbone. To ensure an objective analysis of our findings, we evaluated each proposed model on 3186 images from the public LUNA16 database. Experimental results demonstrated the outperformance of the proposed hybrid Max-Min ViT over the Bilinear Max-Min CNN and the Max-Min CNN, with an accuracy rate of 98.03% versus 96.89% and 95.82%, respectively. This study clearly demonstrated the contribution of the Max-Min strategy in improving the effectiveness of deep learning models for pulmonary nodule classification on CT images.