Classification Analysis of Back propagation-Optimized CNN Performance in Image Processing
Abstract
This study aims to optimize the performance of the Convolutional Neural Network (CNN) in the image classification task by applying data augmentation and fine-tuning techniques to a case study of mammal classification. In this study, we took a fairly complex image classification dataset and used the CNN model as a basis for training and evaluating the performance of the model compared to Back propagation. From this study, the CNN VGG16 architecture optimized with ADAM optimization has been compared with the Back propagation optimization of SGD. We also conducted a literature review on several related studies and basic concepts in CNN, such as convolution, pooling, and fully connected layers. The research methodology involves creating datasets using data augmentation techniques, model training using fine-tuning techniques, and testing model performance using a number of evaluation metrics, including accuracy, precision, and recall. The results of this study indicate that the techniques used have succeeded in improving the performance of the CNN model in complex image classification tasks with accuracy in identifying and monitoring animal species more accurately, with an accuracy of 91.18% for the best model. Model accuracy increased by 2% after applying data augmentation and fine-tuning techniques to the CNN model. These results indicate that the techniques applied in this study can be a good alternative in improving the performance of the CNN model in the image classification task.
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