Detection of small bowel tumor in wireless capsule endoscopy images using an adaptive neuro-fuzzy inference system
Mahdi Alizadeh
Omid Haji Maghsoudi
Kaveh Sharzehi
Hamid Reza Hemati
Alireza Kamali Asl
Alireza Talebpour
摘要：Automatic diagnosis tool helps physicians to evaluate capsule endoscopic examinations faster and more accurate.The purpose of this study was to evaluate the validity and reliability of an automatic post-processing method for identifying and classifying wireless capsule endoscopic images,and investigate statistical measures to differentiate normal and abnormal images.The proposed technique consists of two main stages,namely,feature extraction and classification.Primarily,32 features incorporating four statistical measures (contrast,correlation,homogeneity and energy) calculated from co-occurrence metrics were computed.Then,mutual information was used to select features with maximal dependence on the target class and with minimal redundancy between features.Finally,a trained classifier,adaptive neuro-fuzzy interface system was implemented to classify endoscopic images into tumor,healthy and unhealthy classes.Classification accuracy of 94.2％ was obtained using the proposed pipeline.Such techniques are valuable for accurate detection characterization and interpretation of endoscopic images.
机标关键词：capsule endoscopystatistical measures
分类号：R574.5(消化系及腹部疾病)
论文发表日期：2017-01-01
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：9( 419-427 )
英文信息