Image Classification Using CNN Model Based on Deep Learning

In this work, we will use a convolutional neural network to classify images. In the field of visual image analysis, CNNs (a subset of deep neural networks) are the norm. Multilayer perceptron is used to develop CNN; it is based on a hierarchical model that works on network construction and then delivers to a fully linked layer. All the neurons are linked together and their output is processed in this layer. Here, we demonstrate how our system can get the job done in challenging domains like computer vision by using a deep learning approach. Convolutional Neural Networks (CNNs) are a machine learning method employed by our system for automated picture categorization. For grayscale picture categorization, our method compares to the Digit of MNIST data set. More processing power is needed for picture classification because of the grayscale images in the training data set. Our model's great accuracy in picture classification can be seen in the experimental phase, where we trained it using a convolutional neural network and obtained a result of 98% accuracy