Classification of MNIST Image Dataset Using Improved Convolutional Neural Network
Neela Chattyopadhyay · International Journal for Research in Applied Science and Engineering Technology · 2022
Abstract— Convolutional Neural Network (CNN) holds the current research interest in the ever-evolving image classification field. Accurate classifying the image data with minimum of time is highly desired. But the traditional CNN architecture often fails to generate the appropriate outcome for large dataset. So, a modified approach of CNN is proposed here which is the combination of data augmentation and batch normalization embedded with CNN. Now a days identifying or classifying digits accurately with variety of modes is really a task of challenge. The advantages of the proposed approach are noted when it is applied to the popular MNIST dataset used for digit classification. The proposed approach has been compared with some existing techniques and results infer that the validation, training loss and testing accuracy of the proposed approach are more superior as compared to the state-of-art approaches.