Department of Design, Graduate School of Design, Kyushu University, Fukuoka 815-0000, Japan.
Comput Intell Neurosci. 2022 Apr 30;2022:5951102. doi: 10.1155/2022/5951102. eCollection 2022.
The fast development of image recognition and information technology has influenced people's life and industry management mode not only in some common fields such as information management, but also has very much improved the working efficiency of various industries. In the healthcare field, the current highly disparate doctor-patient ratio leads to more and more doctors needing to undertake more and more patient treatment tasks. Back muscle image detection can also be considered a task in medical image processing. Similar to medical image processing, back muscle detection requires first processing the back image and extracting semantic features by convolutional neural networks, and then training classifiers to identify specific disease symptoms. To alleviate the workload of doctors in recognizing CT slices and ultrasound detection images and to improve the efficiency of remote communication and interaction between doctors and patients, this paper designs and implements a medical image recognition cloud system based on semantic segmentation of CT images and ultrasound recognition images. Accurate detection of back muscles was achieved using the cloud platform and convolutional neural network algorithm. Upon final testing, the algorithm of this system partially meets the accuracy requirements proposed by the requirements. The medical image recognition system established based on this semantic segmentation algorithm is able to handle all aspects of medical workers and patients in general in a stable manner and can perform image segmentation processing quickly within the required range. Then, this paper explores the effect of muscle activity on the lumbar region based on this system.
图像识别和信息技术的快速发展不仅在信息管理等一些常见领域影响了人们的生活和工业管理模式，而且极大地提高了各个行业的工作效率。在医疗保健领域，目前医患比例极不均衡，导致越来越多的医生需要承担越来越多的患者治疗任务。背部肌肉图像检测也可以被认为是医学图像处理中的一项任务。与医学图像处理类似，背部肌肉检测首先需要对背部图像进行处理，并通过卷积神经网络提取语义特征，然后训练分类器来识别特定的疾病症状。为了减轻医生识别 CT 切片和超声检测图像的工作量，提高医生与患者远程沟通和互动的效率，本文设计并实现了一个基于 CT 图像和超声识别图像语义分割的医学图像识别云系统。利用云平台和卷积神经网络算法实现了对背部肌肉的精确检测。经过最终测试，该系统的算法部分满足了所提出的精度要求。基于此语义分割算法建立的医学图像识别系统能够稳定地处理一般医疗工作者和患者的各个方面，并且能够在所需范围内快速进行图像分割处理。然后，本文基于该系统探索了肌肉活动对腰椎区域的影响。
