<![CDATA[This study aims to analyze the impact of kernel size variation in Convolutional Neural Network (CNN) architectures on the performance of animal image classification. The kernel sizes evaluated include 3x3, 5x5, 7x7, and 9x9. Performance was assessed using accuracy metrics and confusion matrix analysis to determine the effectiveness of each model. The results indicate that the 5x5 kernel achieved the highest accuracy and the most balanced classification distribution, while the 9x9 kernel resulted in a significant decline in performance. Excessively large kernels led to the model’s inability to capture local features, causing a high rate of misclassification. In contrast, moderately sized kernels maintained a balance between capturing global context and preserving local detail. These findings highlight the importance of selecting an appropriate kernel size in CNN architecture design to achieve optimal classification results.]]>
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