Comparative Analysis of Neural Network Architectures for Image Classification

Abstract

An important challenge in the growing domains of computer vision and machine learning is the precise categorisation of images, which drives the growth of innovative methodologies and methods. The proposed study will examine three distinct picture classification frameworks. The structures include a Single Layer Network (SLN), a Multi-Layer Perceptron (MLP), and a Convolutional Neural Network (CNN) based on LeCun's technique. The study will employ the MNIST digit identification dataset, the Fashion-MNIST dataset, and the CIFAR-10 dataset. Precision, recall, and F1-score are the measures used for model evaluation. Compared to the SLN and CNN models, the MLP model exhibits superior precision and recall.