Key result
A deep convolutional neural network achieved an image classification accuracy of 98% on the CIFAR-10 dataset containing 60,000 images.
Why the study?
Choosing proper objects or image features for image mining is a key challenge, prompting the design of a higher-performance image classification system.
A deep convolutional neural network achieved 98% accuracy in classifying images from the CIFAR-10 dataset, demonstrating high performance in image mining.
Image mining is the method of searching and discovering valuable information and knowledge from a huge image dataset. Image mining is based on data mining, digital image processing, machine learning, image retrieval, and artificial intelligence. Image mining handled with the hidden information extraction, an association of image data and additional pattern which are not clearly visible in the image. Choosing the proper objects or the feature of the image to be suitable for image mining process is the main challenge would face the programmer. The process includes fine out the most efficient routes at a shorter time and saving the users effort. The main objective of this paper is to design and implement the image classification system with a higher performance, where a CIFAR-10 data set is used to train and testing classification models using CNN. A convolutional neural network is trustworthy, and it could lead to high-quality results. The high accuracy of 98% has been obtained using deep convolutional neural network (DCNN).
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Jasm et al. (2020) studied Image classification (n=60,000). Deep Convolutional Neural Network (DCNN) vs. Previous machine learning algorithms was evaluated on Classification accuracy. A deep convolutional neural network achieved an image classification accuracy of 98% on the CIFAR-10 dataset containing 60,000 images.
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