First steps in clinical implementation of a lung nodule classification method by artificial intelligence

Abstract

Lung cancer remains one of the leading causes of cancer-related mortality worldwide, and early detection is essential to improve patient survival. Computed tomography (CT) is currently the reference imaging modality for lung cancer screening. Recent advances in Artificial Intelligence (AI), particularly Deep Learning (DL), have significantly improved automated medical image analysis and diagnostic accuracy. In this study, we propose a Convolutional Neural Network (CNN)-based approach for pulmonary nodule classification. The model was trained using a merged dataset composed of public CT image databases (IQ-OTH/NCCD and SPIE-AAPM) and real-world CT scans collected at Cheikh Zaid Hospital, Morocco. The experimental dataset included 1,103 malignant images, 508 benign images, and 427 normal images. Contrast Limited Adaptive Histogram Equalization (CLAHE) was applied to enhance image contrast prior to training. The proposed CNN achieved an accuracy of 99.84%, precision of 99.97%, sensitivity of 99.84%, and specificity of 99.82% for a three-class classification task. These results demonstrate the robustness and clinical potential of the model in assisting radiologists with the classification of indeterminate pulmonary nodules. This work represents an initial translational step toward real-world clinical implementation of AI-based lung nodule classification at Cheikh Zaid Hospital and highlights the feasibility of integrating AI tools into routine radiology workflows.