Comparative Study of Convolutional Neural Network Architectures and Optimizers for Flower Image Classification
DOI:
https://doi.org/10.57152/predatecs.v3i2.2110Keywords:
Convolutional Neural Network, DenseNet201, Flower Classification, InceptionV3, MobileNetV2, RMSPropAbstract
This study aims to comparatively evaluate the performance of different Convolutional Neural Network (CNN) architectures and optimization algorithms for flower image classification. Three widely used CNN architectures DenseNet201, InceptionV3, and MobileNetV2 are implemented using transfer learning with pre-trained ImageNet weights and tested with two optimizers, Adam and RMSProp. The experiments are conducted on the Flowers Recognition dataset consisting of five flower classes: daisy, dandelion, rose, sunflower, and tulip. Image normalization and data augmentation are applied to improve model generalization, while performance is evaluated using accuracy, precision, recall, and F1-score. The main contribution of this study lies in a systematic comparison of CNN architectures and optimizers within a unified experimental framework, which is rarely addressed in previous studies. The results show that DenseNet201 combined with the Adam optimizer achieves the highest classification accuracy of 90%, followed by MobileNetV2 with RMSProp, while InceptionV3 yields the lowest accuracy of 85%. These results confirm that the research objective is achieved, demonstrating that both CNN architecture and optimizer selection significantly influence flower image classification performance.
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