Enhanced Gender Classification in Panoramic Dental X-Rays Based on Deep Learning and Hybrid Swarm Algorithm

Gender classification from panoramic dental X-ray images has significant applications in forensic identification and clinical dentistry. This study proposes a novel hybrid approach combining deep learning with metaheuristic optimization for accurate gender prediction. Our primary model employs a fine-tuned DenseNet121 architecture for robust feature extraction from dental radiographs, enhanced by a two-stage feature selection process using Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO). For comprehensive evaluation, we compare this against two alternative implementations: a custom Convolutional Neural Network designed specifically for dental images and a fine-tuned ResNet50 model. The custom CNN processes grayscale images and learns features directly from the dataset, while both DenseNet121 and ResNet50 utilize RGB inputs with transfer learning from pre-trained weights. All extracted features undergo optimization through our hybrid PSO-GWO algorithm before classification with Logistic Regression, ensuring both computational efficiency and model interpretability. Experimental results demonstrate that our DenseNet-based approach achieves superior performance with 95.88 % accuracy, outperforming both the ResNet50 model (93.81%) and custom CNN (91.75%). This performance advantage, establishes the effectiveness of combining deep feature extraction with metaheuristic optimization for dental image analysis. The study provides a practical framework for developing accurate, efficient diagnostic tools in dental forensics and clinical practice.