Image processing is essential across various fields such as health- care, security, remote sensing, forensics, and agriculture, enabling applications like anomaly detection, pattern recognition, scene understanding, and image segmentation. With over 80% of the world's digital data now in visual form, the need for scalable, intelligent solutions is greater than ever. Deep learning (DL) and convolutional neural networks (CNNs) are outperforming untraditional methods in tasks like tumor classification, forgery detection, and object localization with their inherent ability to learn and extract deep feature-level information. Advanced architectural models. We Only Look Once (YOLO), and hybrid models have achieved significant results—CNN-based diagnostic tools now surpass 95% accuracy in detecting cancers, while YOLO variants carry out real-time detection at over 30 FPS with high precision. In the field of image forensics, deep learning models can detect splicing and copy-move forgeries with an accuracy of over 90% by extracting fine-grained artifacts invisible to the human eye. However, the field still poses significant challenges, like the limited availability of annotated datasets, and high computational needs. In high-stakes fields like healthcare, this lack of interpretability raises ethical and practical concerns. Techniques like transfer learning and data augmentation partially improve results on smaller datasets, while Explainable AI (XAI) methods—such as Grad-CAM and SHAP—are becoming essential for model transparency, interpretability, and trustworthiness. Current research is focused on enhancing model-generalizability, interpretability, and fostering interdisciplinary collaboration. As these challenges are progressively overcome, deep learning is expected to fully unlock its transformative potential across diverse image-processing domains.

Digital Image Processing Image Classification Transfer Learning Data Augmentation Feature Extraction