Deep Learning with CNNs: A Survey of Techniques and Real-World Applications

Volume: 11 | Issue: 02 | Year 2025 | Subscription
International Journal of Image Processing and Pattern Recognition
Received Date: 04/28/2025
Acceptance Date: 08/07/2025
Published On: 2025-12-22
First Page: 1
Last Page: 5

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By: Teena Uppal.

Student, Department of Multimedia, BBK DAV College for Women, Amritsar, Punjab, India

Abstract

Convolutional Neural Networks (CNNs) have emerged one of the most powerfully used tools in deep learning, particularly in image and pattern recognition tasks. CNNs are being revolutionized through deep learning resulting in a dominant architecture in computer vision as well as other various domains. This paper provides a comprehensive survey of CNN-based deep learning, its methodologies, regularizations, and computational efficiencies. We also address the challenges and future directions of CNN. This paper provides overview of CNNs. CNNs are differentiated into several layers: input layer, convolutional layer, activation layer, pooling layer, fully connected layer, and the output layer. Over time CNNs evolved significantly. Key methods of training CNNs include data augmentation, transfer layering, and batch normalization as well as additional techniques like dropout and early stopping to improve generalization. CNN has real world applications in security, medical imaging, traffic management, defence, agriculture and pattern recognition. This paper emphasizes the significance of CNN architecture, model evolution, training techniques, and practical use cases. Despite overall development, CNNs continue to evolve and will be even more intelligent and efficient as AI technology advances in future.

CNN (convolutional neural networks), artificial intelligence, real world applications, natural language processing (NLP), rectified linear unit

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Citation:

How to cite this article: Teena Uppal Deep Learning with CNNs: A Survey of Techniques and Real-World Applications. International Journal of Image Processing and Pattern Recognition. 2025; 11(02): 1-5p.

How to cite this URL: Teena Uppal, Deep Learning with CNNs: A Survey of Techniques and Real-World Applications. International Journal of Image Processing and Pattern Recognition. 2025; 11(02): 1-5p. Available from:https://journalspub.com/publication/ijippr/article=22176

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