Deep Convolutional Neural Network–Based Multiclass Classification of Pulmonary Lesions Using Computed Tomography Imagery from the IQ-OTH/NCCD Dataset

Abstract

This study presents an advanced deep learning framework for automated multiclass detection and classification of pulmonary lesions—normal, benign, and malignant—using Computed Tomography imagery from the publicly available the Iraq Oncology Teaching Hospital/National Center for Cancer Diseases chest computed tomography dataset dataset. The proposed model employs a 16-layer convolutional neural network developed by the Visual Geometry Group Network as the primary feature extraction backbone, enhanced through customized preprocessing operations and data augmentation strategies designed to improve model generalization. The dataset consists of 1,190 de-identified Computed Tomography scan images, enabling the model to autonomously learn discriminative radiological features and perform diagnostic classification with reduced dependence on subjective human interpretation. The training pipeline integrates transfer learning, intensity normalization, and class-balanced sampling to mitigate dataset imbalance and strengthen model robustness. Experimental evaluation yielded strong performance outcomes, including an overall classification accuracy of 97.73%, class-specific precision scores of 100% (Benign), 99% (Malignant), and 95% (Normal), and an Area Under the Curve of 99.34%. A confusion matrix analysis further validated the model’s reliability, particularly in the accurate discrimination of malignant lesions from benign and healthy tissue. Comparative analyses against traditional machine learning classifiers demonstrated the superior effectiveness of the 16-layer convolutional neural network developed by the Visual Geometry Group Network based deep transfer learning architecture. The developed framework offers a scalable, cost-efficient, and automated diagnostic support tool for early lung cancer detection. Furthermore, its interpretability is strengthened through the introduction of two novel metrics the Multistage Diagnostic Confidence Index and the Patient Stability Index which provide enhanced transparency in model decision-making. These findings highlight the framework’s substantial potential for integration into clinical decision-support systems and early screening workflows.