Advancements in Orthopaedic Arm Segmentation: A Comprehensive Review

TL;DR

This paper reviews recent advances in medical imaging, focusing on X-ray interpretation using traditional image processing and deep learning techniques like CNNs and YOLOv8, highlighting their practical applications.

Contribution

It provides a comprehensive review of both traditional and deep learning methods for X-ray image segmentation and interpretation, emphasizing their practical utility.

Findings

Deep learning models like YOLOv8 enhance accuracy in X-ray segmentation.

Traditional techniques remain valuable as simple alternatives.

The review aids professionals in understanding digital imaging approaches.

Abstract

The most recent advances in medical imaging that have transformed diagnosis, especially in the case of interpreting X-ray images, are actively involved in the healthcare sector. The advent of digital image processing technology and the implementation of deep learning models such as Convolutional Neural Networks (CNNs) have made the analysis of X-rays much more accurate and efficient. In this article, some essential techniques such as edge detection, region-growing technique, and thresholding approach, and the deep learning models such as variants of YOLOv8-which is the best object detection and segmentation framework-are reviewed. We further investigate that the traditional image processing techniques like segmentation are very much simple and provides the alternative to the advanced methods as well. Our review gives useful knowledge on the practical usage of the innovative and traditional approaches of manual X-ray interpretation. The discovered information will help professionals and researchers to gain more profound knowledge in digital interpretation techniques in medical imaging.