CNN-Based Pet Image Classification: A Deep Learning Approach with Orange
Keywords:
Image Classification, CNN, Orange, Dogs, cats, Machine LearningAbstract
Image classification is a key task in computer vision, with Convolutional Neural Networks (CNNs) excelling at recognizing complex patterns. This study focuses on the binary classification of cat and dog images using Orange Data Mining, a visual programming platform that enables machine learning without coding. A balanced dataset of 1,000 labeled images (500 cats, 500 dogs) from Kaggle was used. Images were embedded into feature vectors using a pretrained ResNet50 model, then classified using two models: a Neural Network (CNN-based) and Naive Bayes. The performance was evaluated using accuracy, precision, recall, F1-score, and a confusion matrix. The CNN model achieved 98% accuracy, with 99.18% precision, 96.80% recall, and an F1-score of approximately 97.9%. Only 20 images were misclassified, indicating strong generalization and low bias. These results confirm the effectiveness of CNNs for pet image classification and demonstrate the value of using pretrained embeddings like ResNet50. The study also highlights Orange’s suitability for deep learning by offering an accessible, low-code environment. This approach is ideal for educational use, prototyping, or real-world applications such as pet recognition and smart surveillance. The findings support broader use of visual programming tools in democratizing AI development.
Keywords: Image Classification, CNN, Orange, Dogs, Cats, Machine Learning
References
Chen, Y. Y. (2020). Dog and Cat Classification with Deep Residual Network. ACM International Conference Proceeding Series, 137–141. https://doi.org/10.1145/3393822.3432321
Dash, S. K., & Mishra, S. (2021). Simultaneous Optimal Placement and Sizing of D-STATCOMs Using a Modified Sine Cosine Algorithm. In Lecture Notes in Networks and Systems: Vol. 202 LNNS. https://doi.org/10.1007/978-981-16-0695-3_41
Demšar, J., & Zupan, B. (2024). Hands-on training about data clustering with orange data mining toolbox. PLoS Computational Biology, 20(12), 1–9. https://doi.org/10.1371/journal.pcbi.1012574
Doungpaisan, P., & Khunarsa, P. (2025). A comparative study of pre-trained models for image feature extraction in weather image classification using orange data mining. Indonesian Journal of Electrical Engineering and Computer Science, 37(1), 241–249. https://doi.org/10.11591/ijeecs.v37.i1.pp241-249
Firmansyah, I., Samudra, J. T., Pardede, D., & Situmorang, Z. (2022). Comparison Of Random Forest And Logistic Regression In The Classification Of Covid-19 Sufferers Based On Symptoms. JOURNAL OF SCIENCE AND SOCIAL RESEARCH, 5(3), 595. https://doi.org/10.54314/jssr.v5i3.994
Habibullah, Ullah, M. O., Kamal, M., Sohag, M. S. H., & Rahman, M. S. (2023). Improved Convolutional Neural Network and Transfer Learning with VGG16 Approach for Image Classification. 2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2023, January 2024, 389–396. https://doi.org/10.1109/3ICT60104.2023.10391458
Hao, Y. (2021). Convolutional Neural Networks for Image Classification. 2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE), May, 342–345. https://doi.org/10.1109/ICAICE54393.2021.00073
Himel, G. M. S., & Islam, M. M. (2025). Benchmark analysis of various pre-trained deep learning models on ASSIRA cats and dogs dataset. Journal of Umm Al-Qura University for Engineering and Architecture, 16(1), 134–149. https://doi.org/10.1007/s43995-024-00094-w
Ichsan, A., Riyadi, S., & Pardede, D. (2024). Analysis of Logistic Regression Regularization in Wild Elephant Classification with VGG-16 Feature Extraction. Journal of Computer Networks, Architecture and High Performance Computing, 6(2), 783–793. https://doi.org/10.47709/cnahpc.v6i2.3789
Mahardi, Wang, I. H., Lee, K. C., & Chang, S. L. (2020). Images Classification of Dogs and Cats using Fine-Tuned VGG Models. 2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020, 230–233. https://doi.org/10.1109/ECICE50847.2020.9301918
Nawrocka, A., Nawrocki, M., & Kot, A. (2023). Research study of image classification algorithms based on Convolutional Neural Networks. Proceedings of the 2023 24th International Carpathian Control Conference, ICCC 2023, 299–302. https://doi.org/10.1109/ICCC57093.2023.10178933
Pardede, D., Firmansyah, I., Handayani, M., Riandini, M., & Rosnelly, R. (2022). Comparison Of Multilayer Perceptron’s Activation And Optimization Functions In Classification Of Covid-19 Patients. JURTEKSI (Jurnal Teknologi Dan Sistem Informasi), 8(3), 271–278. https://doi.org/10.33330/jurteksi.v8i3.1482
Pardede, D., & Hayadi, B. H. (2023). Klasifikasi Sentimen Terhadap Gelaran MotoGP Mandalika 2022 Menggunakan Machine Learning. Jurnal TRANSFORMATIKA, 20(2), 42–50.
Patil, A., & Rane, M. (2021). Convolutional Neural Networks: An Overview and Its Applications in Pattern Recognition. Smart Innovation, Systems and Technologies, 195, 21–30. https://doi.org/10.1007/978-981-15-7078-0_3
Saputra, J., Lawrencya, C., Saini, J. M., & Suharjito, S. (2023). Hyperparameter optimization for cardiovascular disease data-driven prognostic system. Visual Computing for Industry, Biomedicine, and Art, 6(1). https://doi.org/10.1186/s42492-023-00143-6
Seswitha, K. B., Reddy, B. R., Sahithi, S. V., & Priya, M. L. (2023). Comparative Analysis of VGG19, ResNet50, and GoogLeNet Inception Models for BCI. INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT, 07(09). https://doi.org/10.55041/IJSREM25799
Stuti Yadav, & Manish D Sawale. (2023). A review on image classification using deep learning. World Journal of Advanced Research and Reviews, 17(1), 480–482. https://doi.org/10.30574/wjarr.2023.17.1.0064
Veetil, I. K., Gopalakrishnan, E. A., Sowmya, V., & Soman, K. P. (2021). Parkinson’s Disease Classification from Magnetic Resonance Images (MRI) using Deep Transfer Learned Convolutional Neural Networks. Proceedings of the 2021 IEEE 18th India Council International Conference, INDICON 2021, December. https://doi.org/10.1109/INDICON52576.2021.9691745
Wang, Z., Hou, Y., Li, Y., & Liu, Y. (2022). Research on cat and dog recognition based on several CNN models. In P. Loskot (Ed.), International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022) (p. 108). SPIE. https://doi.org/10.1117/12.2660369
Zhai, J., Shen, W., Singh, I., Wanyama, T., & Gao, Z. (2020). A review of the evolution of deep learning architectures and comparison of their performances for histopathologic cancer detection. Procedia Manufacturing, 46(2019), 683–689. https://doi.org/10.1016/j.promfg.2020.03.097
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