Abstract
Abstract: Advancements in computer vision and image processing have fueled a growing interest in understanding the intricate details of skin microstructure for various applications, particularly in the context of aging. This review systematically examines recent developments in skin microstructure segmentation and aging classification methodologies, shedding light on the evolving landscape of research in this domain. Skin aging is a complex process characterized by various morphological and topological changes in the skin microstructure. Analyzing these changes can provide valuable insights into skin health and aging progression. This paper focuses on recent research advancements in skin microstructure segmentation and aging classification using convolutional neural network (CNN)-based models.
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