Deep Ensemble Approach for Adeno-Associated Virus Serotype Classification
Аннотация
Adeno-Associated Virus (AAV) serotype classifica- tion is crucial for gene therapy applications and viral vector engineering. This paper presents a novel ensemble deep learning framework that combines multiple feature extraction architectures with advanced data augmentation techniques to achieve superior classification performance. Our proposed methodology integrates ResNet50V2, InceptionV3, and custom convolutional neural networks as feature extractors, followed by ensemble classification using Support Vector Machines with Bayesian optimization. The framework incorporates advanced augmentation strategies including MixUp and CutMix to enhance model generalization. Experimental results on AAV serotype datasets demonstrate exceptional performance with a mean cross-validation accuracy of 98.61%, significantly outperforming traditional single-model approaches. The system achieves perfect classification (100% accuracy) on multiple validation folds, indicating robust and reliable serotype identification capabilities.