Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/1168
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Öztürk, Şaban | - |
dc.contributor.author | Özkaya, Umut | - |
dc.date.accessioned | 2021-12-13T10:34:47Z | - |
dc.date.available | 2021-12-13T10:34:47Z | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 1532-0464 | - |
dc.identifier.issn | 1532-0480 | - |
dc.identifier.uri | https://doi.org/10.1016/j.jbi.2020.103638 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/1168 | - |
dc.description.abstract | nowadays, considering the number of patients per specialist doctor, the size of the need for automatic medical image analysis methods can be understood. These systems, which are very advantageous compared to manual systems both in terms of cost and time, benefit from artificial intelligence (AI). AI mechanisms that mimic the decision-making process of a specialist increase their diagnosis performance day by day, depending on technological developments. In this study, an AI method is proposed to effectively classify Gastrointestinal (GI) Tract Image datasets containing a small number of labeled data. The proposed AI method uses the convolutional neural network (CNN) architecture, which is accepted as the most successful automatic classification method of today, as a backbone. According to our approach, a shallowly trained CNN architecture needs to be supported by a strong classifier to classify unbalanced datasets robustly. For this purpose, the features in each pooling layer in the CNN architecture are transmitted to an LSTM layer. A classification is made by combining all LSTM layers. All experiments are carried out using AlexNet, GoogLeNet, and ResNet to evaluate the contribution of the proposed residual LSTM structure fairly. Besides, three different experiments are carried out with 2000, 4000, and 6000 samples to determine the effect of sample number change on the proposed method. The performance of the proposed method is higher than other state-of-the-art methods. | en_US |
dc.language.iso | en | en_US |
dc.publisher | ACADEMIC PRESS INC ELSEVIER SCIENCE | en_US |
dc.relation.ispartof | JOURNAL OF BIOMEDICAL INFORMATICS | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Colorectal cancer | en_US |
dc.subject | Gastrointestinal tract | en_US |
dc.subject | CNN | en_US |
dc.subject | LSTM | en_US |
dc.subject | Transfer learning | en_US |
dc.title | Residual LSTM layered CNN for classification of gastrointestinal tract diseases | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1016/j.jbi.2020.103638 | - |
dc.identifier.pmid | PubMed: 33271341 | en_US |
dc.identifier.scopus | 2-s2.0-85097741848 | en_US |
dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü | en_US |
dc.authorid | Ozturk, Saban/0000-0003-2371-8173 | - |
dc.authorwosid | Ozturk, Saban/ABI-3936-2020 | - |
dc.identifier.volume | 113 | en_US |
dc.identifier.wos | WOS:000615920800007 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 57191953654 | - |
dc.authorscopusid | 57191610477 | - |
dc.identifier.scopusquality | Q1 | - |
item.openairetype | Article | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | embargo_20300101 | - |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.author.dept | 02.04. Department of Electrical and Electronics Engineering | - |
Appears in Collections: | Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collections Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections |
Files in This Item:
File | Size | Format | |
---|---|---|---|
1-s2.0-S1532046420302677-main.pdf Until 2030-01-01 | 5.25 MB | Adobe PDF | View/Open Request a copy |
CORE Recommender
SCOPUSTM
Citations
17
checked on Mar 23, 2024
WEB OF SCIENCETM
Citations
45
checked on Mar 23, 2024
Page view(s)
80
checked on Mar 25, 2024
Download(s)
6
checked on Mar 25, 2024
Google ScholarTM
Check
Altmetric
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.