IMPLEMENTING CONVOLUTIONAL NEURAL NETWORK IN IMAGE CLASSIFICATION

Computer vision is a fundamental component of many fields, such as security, healthcare, and manufacturing. The ability to automatically classify images has led to advancements in these areas. One of the most prominent datasets used in this field is the MNIST dataset. CNNs have become the standard in the field of image classification due their exceptional performance in the classification tasks. This paper presents an overview of CNNs' use in this area, and it explores the possibility of using them in the MNIST framework. In addition to developing a CNN model, we also perform various preprocessing procedures. CNNs have been widely used in image classification due to their exceptional performance. This paper explores the use of CNNs in classification with the MNIST dataset. It looks into their strengths and weaknesses, as well as the various architectures they can be used in. The paper aims to provide a framework for developing CNN models for image recognition tasks, with a particular emphasis on the MNIST data. The first part of the paper introduces CNNs and provides a brief overview of their use in this field. The second part of the paper reviews the literature on CNNs in image classification. We trained and validated our CNN model on the MNIST data, and it was able to achieve an accuracy of 99.2%. We then compared our results with those of previous models, and found that our model performed better than the others. We also discussed the limitations and strengths of CNNs in this paper. The paper shows how CNNs perform in image classification with the MNIST dataset. It also provides a roadmap for developing models for this task. In the future, we'll explore various hyperparameters and CNN's architecture to improve their performance.