Comparative Analysis of CNN Architectures (ResNet152V2, InceptionV3, and Xception) in Animal Species Classification
Year: 2025
Type: article
Abstract: This research contributes to the field of computer vision by identifying the most effective Convolutional Neural Network (CNN) architecture for animal species classification, a fundamental task to support biodiversity conservation. A comparative analysis was conducted on three pre-trained models: ResNet152V2, InceptionV3, and Xception. The method used was transfer learning on the "Animals-10" dataset with a two-stage training strategy: feature extraction followed by fine-tuning. The results show that the InceptionV3 and Xception architectures achieved the highest performance with a validation accuracy of 97.30%. Consequently, InceptionV3 is recommended as the most optimal model due to its lowest loss value, indicating the best generalization ability for similar tasks.
Authors: Ferry Khusnil Arief, Nurul Kholidiah, Rifki Mistahul Munir, Rahma Yunita
Institution: Universitas Nahdlatul Ulama Lampung
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4413053166