Dental Biomaterials Research Chair, Dental Health Department, College of Applied Medical Sciences, King Saud University, P.O Box 10219, Riyadh, 11433, Saudi Arabia.
College of Engineering, King Saud University, Riyadh, Saudi Arabia.
J Med Syst. 2018 Sep 10;42(10):192. doi: 10.1007/s10916-018-1049-8.
In the developing technology Charcot-Marie-Tooth (CMT) disease is one of the teeth diseases which are occurred due to the genetic reason. The CMT disease affects the muscle tissue which reduces the progressive growth of the muscle. So, the CMT disease needs to be recognized carefully for eliminating the risk factors in the early stage. At the time of this process, the system handles the difficulties while performing feature extraction and classification part. So, the teeth images are processed by applying the normalization method which eliminates the salt and pepper noise from data. From that, modified group delay function along with Cepstral coefficient features are extracted with effective manner. After that Bacterial Foraging Optimization Algorithm based features are selected. Then the selected features are examined by applying the Bacterial Foraging Optimization Algorithm based spiking neural network which successfully recognizes the CMT disease. At that point the productivity of the framework is assessed with the assistance of exploratory outcomes.
在发展中的技术中，Charcot-Marie-Tooth（CMT）疾病是由于遗传原因引起的牙齿疾病之一。CMT 疾病会影响肌肉组织，从而导致肌肉逐渐萎缩。因此，需要在早期仔细识别 CMT 疾病，以消除风险因素。在这个过程中，系统在执行特征提取和分类部分时会遇到困难。因此，通过应用归一化方法可以从数据中消除椒盐噪声来处理牙齿图像。由此，以有效的方式提取了改进的群延迟函数和倒谱系数特征。然后，基于细菌觅食优化算法选择特征。然后，应用基于细菌觅食优化算法的尖峰神经网络来检查所选特征，从而成功识别 CMT 疾病。此时，借助探索性结果评估框架的效率。
