A SYSTEMATIC REVIEW OF CONVOLUTIONAL NEURAL NETWORK ARCHITECTURES FOR COMPUTER VISION APPLICATIONS
DOI:
https://doi.org/10.62373/a83w5h56Keywords:
CONVOLUTIONAL NEURAL NETWORKS, COMPUTER VISION, DEEP LEARNING, CNN ARCHITECTURES, IMAGE CLASSIFICATIONAbstract
Convolutional Neural Networks (CNNs) have contributed to significant progress in computer vision activities like image classification, object detection, segmentation, and autonomous navigation. This paper is a systematic review of CNN architectures that were posted between 2012 and 2025, examining their development, uses, effectiveness, restrictions and future tendencies. The results also emphasize the move to efficient, scalable, and deployable frameworks, where the emphasis is on lightweight and hybrid frameworks in real-time and edge applications, which can guide the development of intelligent vision systems in the future.
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Copyright (c) 2026 Maitrakkumar Manojkumar Patel, jaipraksh Dwivedi (Author)
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