Automated Hyperparameter Optimization Using Optuna for EfficientNet-Based Medical Image Classification

Manual hyperparameter tuning remains a significant bottleneck in developing robust deep learning models for medical applications. This study presents a comprehensive analysis of Optuna's Tree-structured Parzen Estimator (TPE) for automated hyperparameter optimization of EfficientNet-B2 architecture in Acute Lymphoblastic Leukemia (ALL) cell classification. Using the C-NMC dataset comprising 10,661 training and 1,867 test images, we conducted 20 optimization trials with architecture-specific search spaces targeting learning rate (1×10⁻⁵ to 1×10⁻²), dropout rates (0.1-0.5), weight decay (1×10⁻⁶ to 1×10⁻²), and hidden layer sizes (256-1024 neurons). Results demonstrate that learning rate dominates optimization importance (55%) followed by dropout regularization (34%). The framework achieved optimal configuration with 96.86% validation accuracy, reducing manual tuning time by approximately 90% while maintaining its performance (86.72% test accuracy, 0.92 AUC-ROC). Statistical analysis across multiple runs shows consistent performance with coefficient of variation of 1.96%, validating the reliability of TPE-based optimization for medical imaging applications.