Comparative Study of AI-Based Deep Learning Models for Image Classification and Recognition: Review
Volume: 14 - Issue: 12 - Date: 01-12-2025
Approved ISSN: 2278-1412
Published Id: IJAECESTU475 | Page No.: 101-109
Author: Narayan Datt Tiwari
Co- Author: Kamlesh Raghuwanshi,Dr. Surabhi Karsoliya,,
Abstract:-Image classification and recognition are important areas of computer vision that help machines identify
objects, patterns, and features in images. These technologies are widely used in many fields such as healthcare,
agriculture, environmental monitoring, and security. Over time, methods have moved from traditional approaches,
which depended on manual feature selection, to advanced deep learning approaches that can automatically learn
patterns from large amounts of data. This review paper discusses the latest trends in deep learning for image
classification, including the use of hybrid models, lightweight designs for real-world use, and techniques that
improve both efficiency and accuracy. It also highlights how these methods are applied in real scenarios like
disease detection in plants, medical diagnosis, and intelligent surveillance. At the same time, challenges remain,
such as the need for large datasets, high computational power, and the difficulty in explaining how models make
decisions.
Key Words:-Image Classification, Image Recognition, Deep Learning, Computer Vision, Artificial Intelligence, Transfer Learning, Lightweight Models, Applications
Area:-Engineering
DOI Member: 14.63.476
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