Convolutional Neural Network (CNN) Model for Multi-Category Classification of Animals

Abstract

The progress in artificial intelligence (AI) technology has led to substantial transformations in several areas, particularly in image recognition and classification.  Convolutional neural networks (CNNs) are widely recognized as powerful methods in the field of digital image processing, particularly for tasks involving pattern recognition and image classification. CNNs extract essential features from images through convolution and pooling operations, followed by fully connected layers that produce classification output. This research aims to train a CNN model to classify animal images into categories such as cats, rabbits, cows, chickens and others. Image classification is crucial for practical applications, including image database management, visual search, and automated recognition systems. Using a labeled dataset, the CNN model is trained to recognize and classify images based on distinctive visual features characteristic of each category. The training process involves data preprocessing, network architecture implementation, and hyperparameter optimization. Model performance is evaluated using metrics like accuracy, precision, recall, and F1-score to ensure accurate and reliable classification results. Result concludes that CNN is a highly effective approach for classifying animal images, achieving a loss rate of 0.1949 and an accuracy of 95.45%.