Advancements and Applications of Convolutional Neural Networks in Image Analysis: A Comprehensive Review

Hewa Majeed Zangana; Ayaz Khalid Mohammed; Firas Mahmood Mustafa

doi:10.58602/jics.v3i1.30

Hewa Majeed Zangana IT Dept., Duhok Technical College, Duhok Polytechnic University, Duhok, Iraq
Ayaz Khalid Mohammed Computer System Department, Ararat Technical Private Institute
Firas Mahmood Mustafa Chemical Engineering Dept., Technical College of Engineering, Duhok Polytechnic University

DOI: https://doi.org/10.58602/jics.v3i1.30

Keywords: Convolutional Neural Networks (CNNs); image analysis; architectures; training strategies; applications; challenges; future directions.

Abstract

Convolutional Neural Networks (CNNs) have revolutionized image analysis, extracting meaningful features from raw pixel data for accurate predictions. This paper reviews CNN fundamentals, architectures, training methods, applications, challenges, and future directions. It introduces CNN basics, including convolutional and pooling layers, and discusses diverse architectures like LeNet, AlexNet, ResNet, and DenseNet. Training strategies such as data preprocessing, initialization, optimization, and regularization are explored for improved performance and stability. CNN applications span healthcare, agriculture, ecology, remote sensing, and security, enabling tasks like object detection, classification, and segmentation. However, challenges like interpretability, data bias, and adversarial attacks persist. Future research aims to enhance CNN robustness, scalability, and ethical deployment. In conclusion, CNNs drive transformative advancements in image analysis, with ongoing efforts to address challenges and shape the future of AI-enabled technologies.

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