Segmentation and Classification of Stomach Abnormalities Using Deep Learning  

作　　者：Javeria Naz Muhammad Attique Khan Majed Alhaisoni Oh-Young Song Usman Tariq Seifedine Kadry 

机构地区：[1]Department of Computer Science,HITEC University Taxila,Taxila,Pakistanl 2College of Computer Science and Engineering,University of Ha’il,Ha’il,Saudi Arabial 3Department of Software,Sejong University,Seoul,Korea,Gwangjin-gu,Koreal 4College of Computer Engineering and Sciences,Prince Sattam Bin Abdulaziz University,Al-Khraj,Saudi Arabial 5Faculty of Applied Computing and Technology,Noroff University College,Kristiansand,Norway

出　　处：《Computers, Materials & Continua》2021年第10期607-625,共19页计算机、材料和连续体（英文）

基　　金：This research was financially supported in part by the Ministry of Trade,Industry and Energy(MOTIE)and Korea Institute for Advancement of Technology(KIAT)through the International Cooperative R&D program.(Project No.P0016038);in part by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2021-2016-0-00312)supervised by the IITP(Institute for Information&communications Technology Planning&Evaluation).

摘　　要：An automated system is proposed for the detection and classification of GI abnormalities.The proposed method operates under two pipeline procedures:(a)segmentation of the bleeding infection region and(b)classification of GI abnormalities by deep learning.The first bleeding region is segmented using a hybrid approach.The threshold is applied to each channel extracted from the original RGB image.Later,all channels are merged through mutual information and pixel-based techniques.As a result,the image is segmented.Texture and deep learning features are extracted in the proposed classification task.The transfer learning(TL)approach is used for the extraction of deep features.The Local Binary Pattern(LBP)method is used for texture features.Later,an entropy-based feature selection approach is implemented to select the best features of both deep learning and texture vectors.The selected optimal features are combined with a serial-based technique and the resulting vector is fed to the Ensemble Learning Classifier.The experimental process is evaluated on the basis of two datasets:Private and KVASIR.The accuracy achieved is 99.8 per cent for the private data set and 86.4 percent for the KVASIR data set.It can be confirmed that the proposed method is effective in detecting and classifying GI abnormalities and exceeds other methods of comparison.

关 键 词：Gastrointestinal tract contrast stretching SEGMENTATION deep learning features selection 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]