Transfer learning with fuzzy decision support for multi-class lung disease classification: performance analysis of pre-trained CNN models

Accurate and efficient classification of lung diseases from medical images remains a significant challenge in computer-aided diagnosis systems. This research presents a novel approach integrating transfer learning techniques with fuzzy decision support systems for multi-class lung disease classification. We compare the performance of three pre-trained CNN architectures-VGG16, VGG19, and ResNet50-enhanced with a fuzzy logic decision layer. The proposed methodology employs transfer learning to leverage knowledge from large-scale datasets while adapting to the specific characteristics of lung disease images. A k-symbol Lerch transcendent function is implemented for image enhancement during preprocessing, significantly improving feature extraction capabilities by 23.4% in contrast enhancement and 18.7% in feature visibility. The fuzzy decision support system addresses inherent uncertainties in medical image classification through membership functions and rule-based inference mechanisms specifically designed for lung pathology features. Experimental evaluation was conducted on a comprehensive dataset of 8,409 chest X-ray images across six disease classes: COVID-19, Pneumonia, Tuberculosis, Lung Opacity, Cardiomegaly, and Normal cases. Results demonstrate that the ResNet50-based model with fuzzy integration achieves superior classification accuracy of 98.7%, sensitivity of 98.4%, and specificity of 98.8%, outperforming standard implementations of VGG16 (97.8% accuracy) and VGG19 (98.2% accuracy). The proposed approach shows particular strength in handling borderline cases where traditional CNN confidence falls below 75%, achieving 8.4% improvement in uncertain case classification. Statistical significance testing confirms meaningful performance gains (p < 0.05) across all architectures, with ResNet50 showing the most substantial enhancement (p = 0.0018). The fuzzy inference system activates an average of 8.4 rules per classification decision, providing transparent reasoning pathways that enhance clinical interpretability while maintaining real-time processing capability (0.23 s per image). This research contributes to advancing automated lung disease diagnosis systems with improved accuracy, uncertainty handling, and clinical interpretability for computer-aided diagnostic applications.