COMPARATIVE PERFORMANCE ANALYSIS OF CNN AND MLP ON CIFAR-10 USING PYTHON: A DETAILED EVALUATION AND INTERPRETATION

Abstract

In this study, we compare the performance of Convolutional Neural Networks (CNNs) and Multilayer Perceptrons (MLPs) on the CIFAR-10 dataset, a benchmark dataset widely used for image classification tasks. We implement both models using Python and Keras, evaluate them based on various performance metrics, and analyze their strengths and weaknesses. Our findings indicate that CNNs significantly outperform MLPs in terms of accuracy, precision, recall, and F1-score, highlighting the advantages of CNNs in handling image data through spatial hierarchies and local patterns.