Saranya A, Kottursamy Kottilingam, AlZubi Ahmad Ali, Bashir Ali Kashif
Department of Computational Intelligence, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Tamilnadu India.
Computer Science Department, Community College, King Saud University, P.O. Box 28095, Riyadh, 11437 Saudi Arabia.
Soft comput. 2022;26(16):7519-7533. doi: 10.1007/s00500-021-06519-1. Epub 2021 Dec 1.
Predictive health monitoring systems help to detect human health threats in the early stage. Evolving deep learning techniques in medical image analysis results in efficient feedback in quick time. Fibrous dysplasia (FD) is a genetic disorder, triggered by the mutation in Guanine Nucleotide binding protein with alpha stimulatory activities in the human bone genesis. It slowly occupies the bone marrow and converts the bone cell into fibrous tissues. It weakens the bone structure and leads to permanent disability. This paper proposes the study of FD bone image analyzing techniques with deep networks. Also, the linear regression model is annotated for predicting the bone abnormality levels with observed coefficients. Modern image processing begins with various image filters. It describes the edges, shades, texture values of the receptive field. Different types of segmentation and edge detection mechanisms are applied to locate the tumor, lesion, and fibrous tissues in the bone image. Extract the fibrous region in the bone image using the region-based convolutional neural network algorithm. The segmented results are compared with their accuracy metrics. The segmentation loss is reduced by each iteration. The overall loss is 0.24% and the accuracy is 99%, segmenting the masked region produces 98% of accuracy, and building the bounding boxes is 99% of accuracy.
预测性健康监测系统有助于在早期阶段检测人类健康威胁。医学图像分析中不断发展的深度学习技术能够在短时间内产生有效的反馈。骨纤维发育不良（FD）是一种遗传性疾病，由人类骨骼生成中具有α刺激活性的鸟嘌呤核苷酸结合蛋白的突变引发。它会缓慢占据骨髓并将骨细胞转化为纤维组织。它会削弱骨骼结构并导致永久性残疾。本文提出了用深度网络研究FD骨图像分析技术。此外，还对线性回归模型进行了注释，以通过观察到的系数预测骨异常水平。现代图像处理从各种图像滤波器开始。它描述了感受野的边缘、阴影、纹理值。应用不同类型的分割和边缘检测机制来定位骨图像中的肿瘤、病变和纤维组织。使用基于区域的卷积神经网络算法提取骨图像中的纤维区域。将分割结果与其准确性指标进行比较。每次迭代都会降低分割损失。总体损失为0.24%，准确率为99%，分割掩码区域的准确率为98%，构建边界框的准确率为99%。
