Comparative Analysis of CNN Architectures (ResNet152V2, InceptionV3, and Xception) in Animal Species Classification
DOI:
https://doi.org/10.55927/marcopolo.v3i7.84Keywords:
Animal Classification, Convolutional Neural Network, Transfer Learning, InceptionV3, Comparative AnalysisAbstract
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.
References
Abdel-Jaber, H., Devassy, D., Al Salam, A., Hidaytallah, L., & El-Amir, M. (2022). A Review of Deep Learning Algorithms and Their Applications in Healthcare. In Algorithms (Vol. 15, Issue 2). MDPI. https://doi.org/10.3390/a15020071
Agarwal, N., Sondhi, A., Chopra, K., & Singh, G. (2021). Transfer learning: Survey and classification. Advances in Intelligent Systems and Computing, 1168, 145–155. https://doi.org/10.1007/978-981-15-5345-5_13
Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021). Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8(1). https://doi.org/10.1186/s40537-021-00444-8
Dahr, J. M., & Gaafar, A. S. (2024). Performance Evaluation of the Convolutional Neural Networks for Object Identification Using RGB and Binary Images. Informatica (Slovenia), 48(21), 177–188. https://doi.org/10.31449/inf.v48i21.6568
Dwiatmoko, F., Utami, D., Sivi, N. A., Nahdlatul, U., & Lampung, U. (2024). Klasifikasi Citra Sampah Organik dan Non Organik Menggunakan Algoritma CNN (Convolutional Neural Network).
Hosna, A., Merry, E., Gyalmo, J., Alom, Z., Aung, Z., & Azim, M. A. (2022). Transfer learning: a friendly introduction. Journal of Big Data, 9(1). https://doi.org/10.1186/s40537-022-00652-w
Huda, N., Mahiruna, A., Sulistijanti, W., Chandra Noor Santi, R., Teknologi Statistika dan Bisnis Muhammadiyah Semarang Jl DrHamka, I. K., Kota Semarang Jawa Tengah, N., & Stikubank Semarang Jl Tri Lomba Juang Mugassari Kota Semarang Jawa Tengah, U. (2023). Analisis Performa Inceptionv3 Convolutional Network Pada Klasifikasi Varietas Daun Grapevine Performance Analysis of InceptionV3 Convolutional Network Used for Grapevine Leaves Varieties Classification. In Jurnal Sains Komputer dan Teknologi Informasi e-issn (Vol. 5, Issue 2).
Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. https://doi.org/10.1007/s12525-021-00475-2/Published
Mathew, M. P., .M, S. E., VP, J., & Mahesh, T. Y. (2023). Pretrained CNN Architectures: A Detailed Analysis Using Bell Pepper Image Datasets. https://doi.org/10.21203/rs.3.rs-3146418/v1
Mienye, I. D., & Swart, T. G. (2024). A Comprehensive Review of Deep Learning: Architectures, Recent Advances, and Applications. Information (Switzerland), 15(12). https://doi.org/10.3390/info15120755
Mohamed, A. H., Refaat, M., & Hemeida, A. M. (2022). Image classification based deep learning: A Review. Aswan University Journal of Science and Technology, 2(1). https://journals.aswu.edu.eg/stjournal
Ridhovan, A., Suharso, A., Fakultas, ), Komputer, I., Karawang, S., Ronggo Waluyo, J. H., Timur, T., & Karawang, K. (2022). Penerapan Metode Residual Network (Resnet) Dalam Klasifikasi Penyakit Pada Daun Gandum.
Shafik, W., Tufail, A., Liyanage De Silva, C., & Awg Haji Mohd Apong, R. A. (2025). A novel hybrid inception-xception convolutional neural network for efficient plant disease classification and detection. Scientific Reports, 15(1), 3936. https://doi.org/10.1038/s41598-024-82857-y
Sharifani, K., & Amini, M. (2023). Machine Learning and Deep Learning: A Review of Methods and Applications. In World Information Technology and Engineering Journal (Vol. 10). https://ssrn.com/abstract=4458723
Xu, X., Zhao, M., Shi, P., Ren, R., He, X., Wei, X., & Yang, H. (2022). Crack Detection and Comparison Study Based on Faster R-CNN and Mask R-CNN. Sensors, 22(3). https://doi.org/10.3390/s22031215
