Published Paper Details:

In recent times, a significant transformation in the field of processing the medical image is due to advancements in deep learning techniques by providing powerful tools for feature extraction, classification, segmentation, and generation. In this document, an extensive examination is conducted on the utilization of AI with deep methodologies in a range of image processing tasks. We begin by discussing the fundamentals of machine learning subset, entailing artificial neural networks(ANNs), generative adversarial networks (GANs), recurrent neural networks (RNNs), fully convolutional networks (FCNs), and convolutional neural networks (CNNs), highlighting their strengths and weaknesses in image analysis. Afterwards, we explore various practical uses of deep learning in the field of image processing. These include tasks like identifying objects within images, classifying images, segmenting images based on their content, enhancing image resolution, and transferring artistic styles onto images. Furthermore, we discuss challenges and future directions in leveraging advanced techniques in artificial intelligence, specifically deep learning, which are being utilized to enhance image processing, including interpretability, robustness, and scalability. By synthesizing existing literature and presenting insights into the latest advancements, this review serves as a valuable resource for researchers, practitioners, and enthusiasts seeking to harness the potential of deep learning for solving real-world image processing problems