Knee Osteoarthritis SCAENet: Adaptive Knee Osteoarthritis Severity Assessment Using Spatial Separable Convolution with Attention-Based Ensemble Networks with Hybrid Optimization Strategy.

Sriramulu Devarapaga, Rajesh Thumma
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Abstract

Osteoarthritis (OA) of the knee is a chronic state that significantly lowers the quality of life for its patients. Early detection and lifetime monitoring of the progression of OA are necessary for preventive therapy. In the course of therapy, the Kellgren and Lawrence (KL) assessment model categorizes the rigidity of OA. Deep techniques have recently been used to increase the precision and effectiveness of OA severity assessments. The training process is compromised by low-confidence samples, which are less accurate than normal ones. In this work, a deep learning-based knee osteoarthritis severity assessment model is recommended to accurately identify the condition in patients. The phases of the designed model are data collection, feature extraction, and prediction. At first, the images are generally gathered from online resources. The gathered images are given into the feature extraction phase. A new model is implemented to predict knee osteoarthritis named Spatial Separable Convolution with Attention-based Ensemble Networks (SCAENet), which includes feature extraction, stacked target-based feature pool generation, and knee osteoarthritis prediction. The feature extraction is done using ResNet, Visual Geometry Group (VGG16), and DenseNet. The stacked target-based feature pool is obtained from the SCAENet. Hence, the stacked target-based feature pool is obtained by the Hybridization of Equilibrium Slime Mould with Bald Eagle Search Optimization (HESM-BESO). Here, the knee osteoarthritis's severity prediction is performed using the dimensional convolutional neural network (1DCNN) technique. The designed SCAENet model is validated with other conventional methods to show high performance.

膝骨关节炎 SCAENet:利用空间可分离卷积与基于注意力的集合网络和混合优化策略进行自适应膝关节骨关节炎严重程度评估。
膝关节骨性关节炎(OA)是一种慢性疾病,大大降低了患者的生活质量。早期发现并终生监测 OA 的进展对于预防性治疗十分必要。在治疗过程中,凯尔格伦和劳伦斯(Kellgren and Lawrence,KL)评估模型对 OA 的硬度进行分类。最近,深度技术被用于提高 OA 严重性评估的精确度和有效性。在训练过程中,低置信度样本会影响训练效果,其准确性低于正常样本。在这项工作中,建议使用基于深度学习的膝关节骨关节炎严重程度评估模型来准确识别患者的病情。设计的模型分为数据收集、特征提取和预测三个阶段。首先,一般从网上资源收集图像。收集到的图像进入特征提取阶段。该模型包括特征提取、基于堆叠目标的特征池生成和膝关节骨关节炎预测。特征提取使用 ResNet、Visual Geometry Group (VGG16) 和 DenseNet 完成。基于堆叠目标的特征库来自 SCAENet。因此,基于堆叠目标的特征库是通过平衡粘液模与秃鹰搜索优化(HESM-BESO)杂交获得的。在这里,膝骨关节炎的严重程度预测是通过维卷积神经网络(1DCNN)技术来实现的。所设计的 SCAENet 模型与其他传统方法进行了验证,显示出很高的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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