摘要
机器人与机器学习每日新闻的一位新闻记者兼编辑-机器学习-模式识别和人工智能的最新研究结果已经发表。根据NewsRx Comresponden TS来自印度班加鲁的新闻报道，研究表明：“在医学领域，由于骨骼的刚性，分析各种骨骼结构是至关重要的。X射线成像在包括骨龄评估、骨折检测和I mplant创建在内的医疗过程中发挥着至关重要的作用。”我们的新闻编辑从班加罗尔大学的研究中得到一句话:“然而,操作者的参与会带来偏见,增加处理时间。自动化过程可以通过最小化偏见和操作者的参与来减少处理时间,提高诊断的准确性。”一种基于编码-解码的轻量级手骨多分类分割方法。主要目的是协助医生进行骨龄分析、骨折检测、手部运动分析和I mplant设计。研究目标分为三个阶段，主要集中在我们目标的第一阶段，即从组织中描绘骨骼，研究骨骼结构，该模型以DenseNet121为特征提取器,以sigmoid-weight Ted线性单元(SiLU)为激活函数,实验结果表明,该模型在手骨多类分割中具有较好的分割效果,平均分割率为85.02%,方块分数为92.2%。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing - Pattern Recognition and Artificial Intelligence have been published. Accor ding to news reporting originating from Bengaluru, India, by NewsRx corresponden ts, research stated, “In the medical field, analyzing various bone structures is crucial due to the rigid nature of bones. X-ray imaging plays an essential role in medical procedures, including bone age evaluation, fracture detection, and i mplant creation.” Our news editors obtained a quote from the research from Bangalore University, “ However, operator involvement can introduce biases and increase processing time. Automating the process could reduce processing time and enhance diagnostic accu racy by minimizing biases and operator involvement. This paper introduces the Re fined DeepLab model, a lightweight encoder-decoder-based approach for multiclass segmentation of hand bones. The primary objective is to assist physicians in ta sks such as bone age analysis, fracture detection, hand movement analysis, and i mplant design. The research objectives are organized into three phases, with thi s work focusing on the first phase of our objectives, which is delineating bones from tissues, studying the bone structure, and multiclass segmentation of hand bones. The model utilizes DenseNet121 as its feature extractor and Sigmoid-weigh ted Linear Unit (SiLU) as its activation function. Experimental findings demonst rate promising performance in hand bone multiclass segmentation, with a Mean Int ersection over Union (mIoU) of 85.02% and a Dice score of 92.2% .”
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