Paper Info
Title
Developing Charcot-Marie-Tooth Disease Recognition System Using Bacterial Foraging Optimization Algorithm Based Spiking Neural Network.
Abstract
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.
Year
DOI
Venue
2018
10.1007/s10916-018-1049-8
J. Medical Systems
Keywords
Field
DocType
Bacterial foraging optimization algorithm based spiking neural network,Charcot–Marie–tooth (CMT) disease,Modified group delay function,Normalization process
Data mining,Normalization (statistics),Recognition system,Pattern recognition,Cepstrum,Salt-and-pepper noise,Feature extraction,Optimization algorithm,Artificial intelligence,Spiking neural network,Medicine,Foraging
Journal
Volume
Issue
ISSN
42
10
0148-5598
Citations 
PageRank 
References 
1
0.34
4
Authors
3
Name
Order
Citations
PageRank
Abdulaziz A. Al Kheraif111.35
Mohamed Hashem211.02
Mohammed Sayed S. Al Esawy310.34