Preprocessing of Medical Images using Deep Learning: A Comprehensive Review

Medical image preprocessing is a vital component in improving the quality and interpretability of medical images, which directly impacts accurate diagnosis and treatment planning. The advancements in deep learning techniques have paved the way for creative solutions to the problems posed by medical image preprocessing. This review article seeks to offer a thorough examination of contemporary deep-learning methods used for the preprocessing of medical images. The study delves into various preprocessing tasks, including denoising, image enhancement, registration, and segmentation, and examines how deep learning techniques have effectively tackled these tasks. It highlights the significant contributions of deep learning-based preprocessing methods in enhancing image quality, reducing noise, improving contrast, and extracting precise anatomical structures. Moreover, the study emphasizes the impact of deep learning-based preprocessing methods on clinical decision-making. By improving the quality and interpretability of medical images, these techniques empower radiologists and clinicians to make accurate diagnoses, plan treatments, and monitor disease progression more effectively. The review also explores the potential applications of deep learning-based preprocessing methods across different clinical imaging practices, including CT, ultrasound, and MRI.