Enhancing Image Classification Using a Convolutional Neural Network Model

In recent years, with the rapid development of the current classification system in digital content identification, automatic classification of images has become the most challenging task in the field of computer vision. As can be seen, vision is quite challenging for a system to automatically understand and analyze images, as compared to the vision of humans. Some research papers have been done to address the issue in the low-level current classification system, but the output was restricted only to basic image features. However, similarly, the approaches fail to accurately classify images. For the results expected in this field, such as computer vision, this study proposes a deep learning approach that utilizes a deep learning algorithm. In this research, a proposed model based on a Convolutional Neural Network (CNN) which is a machine learning tool that can be used for the automatic classification of images. The model is concerned with the classification of images, and for this, it employs the COREL Image dataset (Corel Gallery Image Dataset) as a reference. The images in the dataset used for training are harder than the classification of the images since they need more computational resources. In the experimental part, training the images using the CNN network achieved 98.52% accuracy, proving that the model has high accuracy in the classification of images.