Charting New Frontiers: Insights and Future Directions in ML and DL for Image Processing

This review and methodology framework looks at the current use of machine learning (ML) and deep learning (DL) for processing images, both medical and non‑medical. It examines recent progress in convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformer‑based models, and then compares the leading approaches. The study tackles major hurdles such as high computational cost, limited data, and the difficulty of interpreting model decisions. It also offers step‑by‑step guidance for building reliable pipelines that perform image segmentation, classification, and restoration. Special attention is given to medical imaging methods like MRI, CT, and histopathology, where accurate diagnosis is critical. At the same time, the framework looks at non‑medical fields

such as autonomous navigation and remote sensing to find useful overlaps in techniques. Through careful simulation and performance testing on a variety of datasets, the work creates a baseline that can guide future research. It provides a clear roadmap for creating scalable, high‑precision image‑processing systems and serves as a solid reference for choosing, tuning, and validating models in advanced computer‑vision projects.

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