Aim: This study aims to classify images using VGG19 algorithm with high accuracy and comparing it with the CNN algorithm. Materials and methods: A sample size of 80,000 images is taken from the CIFAR-10 dataset and it is divided into a training dataset (n=64000 (80%)) and a test dataset (n=16000 (20%)). The performance of the classification is compared using two groups namely VGG19 and CNN algorithms. Results: The object classification accuracy for VGG19 and CNN are 91.32 % and 88.93 % respectively. The VGG19 provided the best accuracy value compared to the CNN algorithm with a significance of p = 0.002 (p < 0.05). Conclusion: It is observed that the VGG19 algorithm performed significantly better than the CNN algorithm in image classification.

Image classification, Image segmentation, VGG19 algorithm, CNN algorithm, Machine learning, Novel preprocessing