DEEP LEARNING TECHNIQUES FOR IMAGE RECOGNITION: A COMPARATIVE STUDY

Abstract

Deep learning has revolutionized the field of computer vision, particularly in the domain of image recognition. With the advent of powerful neural network architectures, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and more recently Transformer-based models, significant advancements have been made in classification accuracy, feature extraction, and real-time recognition capabilities. This study presents a comparative analysis of various deep learning techniques applied to image recognition, highlighting their architectures, strengths, limitations, and performance benchmarks on standard datasets. Special attention is given to training efficiency, computational requirements, and scalability, which are critical factors in practical deployment. The findings demonstrate that while CNN-based architectures remain dominant in structured image recognition tasks, hybrid and Transformer-based models show great promise for handling complex, large-scale visual data. This comparative study contributes to a deeper understanding of how different approaches can be leveraged to optimize image recognition systems across diverse applications such as medical imaging, autonomous vehicles, and security surveillance.