Applications of deep learning in automated ‎image classification: a review - Sh. Othman, Renjbar; Hajar Maseeh Yasin, (International Journal of Scientific World, 2025)
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.