Prediction of Rheumatoid Arthritis using Deep Learning Techniques
Prediction of Rheumatoid Arthritis using Deep Learning Techniques
复制标题
使用深度学习技术预测类风湿关节炎
DOI：
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
B. Kanisha
中科院分区：
文献类型：
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作者：
Siddharth Ojha;Siddharth Anand;B. Kanisha
类风湿性关节炎(RA)是一种免疫系统攻击身体内健康细胞的疾病。受RA影响的身体部位会肿胀。RA的主要焦点区域是关节，通常是同时有多个关节。最常受类风湿关节炎影响的关节是手腕、膝盖和手部的关节。在含有类风湿关节炎的关节中，关节组织会受到影响，因为关节衬里存在肿胀。组织损伤可能导致长期或慢性疼痛、颤抖和畸形。在过去的几年里，深度学习技术帮助识别和预测疾病变得容易。这项研究讨论了一种更准确地识别关节炎的方法。在这项研究中，标准的CNN模型被用来预测关节炎，ResNet CNN和AlexNet CNN。建议的数据集包含从各种不同来源获得的654多张图像。为了提高数据集中的图像质量，对数据集进行了图像预处理和图像分割。该方法的准确率约为97.5%。
Rheumatoid Arthritis (RA), is a disorder where the immune system attacks the cells in the body which are healthy. Those parts of the body which are affected by RA get swelled up. The main area of focus for The RA is the joints, generally many joints at the same time. The joints which are most often affected by RA are the joints which are present in the wrist, knees and hands. Joint tissue gets affected in a joint containing RA because of swelling present in the lining of the joint. Tissue damage may lead to long-lasting or chronic pain, shakiness, and deformity. Over the past few years deep learning techniques has helped identifying and predicting diseases become easily. This study discusses about a more accurate way of identifying arthritis. In this study, standard CNN model is proposed to predict arthritis, ResNet CNN and AlexNet CNN. Proposed dataset contains over 654 images obtained from various different sources. To enhance the quality of images in the dataset image preprocessing and image segmentation has been performed. An accuracy of around 97.5% has been obtained from this method.