Applications of deep learning in automated ‎image classification: a review

  • Authors

    • Renjbar Sh. Othman MSc student
    • Hajar Maseeh Yasin Akre University for Applied Science, Technical College of Informatics, Akre, Department of Information Technology, Duhok, Kurdistan Region, Iraq

    Received date: February 19, 2025

    Accepted date: March 6, 2025

    Published date: March 11, 2025

    https://doi.org/10.14419/qra5nc04
  • Automated Image Classification; Deep Learning; Convolutional Neural Networks; Transfer ‎Learning; Medical Image Analysis; Advanced Pre-processing Techniques.

  • Abstract

    Deep learning's developments have greatly changed automatic picture categorization, proving ‎great accuracy and efficiency in many fields. Medical imaging, agriculture, and environmental ‎monitoring are just a few innovative uses for techniques including hybrid models, transfer ‎learning, and convolutional neural networks (CNNs). Improved precision using ensemble and ‎attention-based models has helped medical diagnosis including diabetic retinopathy and breast ‎cancer detection benefit. Likewise, CNNs are used in environmental monitoring systems for ‎species identification and wind turbine inspection. Although these developments highlight the ‎transforming power of deep learning, problems still exist including limited datasets, computing ‎requirements, and lack of generalizability across many settings. Researchers support lightweight ‎model designs, innovative augmentation techniques, and dataset extension to help solve these ‎challenges. Future developments in architectures and cross-domain applications have great ‎potential to widen the range of efficiency of deep learning in automated picture categorization. ‎This analysis underlines the need for ongoing research to fully utilize deep learning in the solution ‎of challenging categorization tasks‎.

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  • How to Cite

    Sh. Othman, R., & Hajar Maseeh Yasin. (2025). Applications of deep learning in automated ‎image classification: a review. International Journal of Scientific World, 11(1), 104-113. https://doi.org/10.14419/qra5nc04

    Received date: February 19, 2025

    Accepted date: March 6, 2025

    Published date: March 11, 2025