Deep Learning Models for Image Classification: A Comparison
DOI:
https://doi.org/10.21512/emacsjournal.v7i3.14317Keywords:
Image, Classification, Deep learningAbstract
This study evaluates the performance of four deep learning CNN models—MobileNetV2, DenseNet121, EfficientNetB0, and InceptionV3—for image classification using three datasets: animal images, MNIST-digit images, and flower images. The models were assessed based on accuracy, precision, recall, and F1-score using a confusion matrix. Results indicate that DenseNet121 and MobileNetV2 achieved the highest accuracy. However, DenseNet121 had the longest training time, while MobileNetV2 was the most efficient, offering the fastest training and testing times. This suggests that MobileNetV2 is ideal for real-time applications where speed is critical, while DenseNet121 is better suited for tasks prioritizing accuracy despite longer training. EfficientNetB0 and InceptionV3 demonstrated balanced performance, but with trade-offs in speed and accuracy. These findings highlight the importance of selecting CNN models based on dataset characteristics and computational constraints. A well-chosen model can significantly impact performance, especially in applications where real-time processing or high precision is required. This study emphasizes the need for an optimal balance between efficiency and accuracy in deep learning-based image classification
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