Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/1168
Title: Residual LSTM layered CNN for classification of gastrointestinal tract diseases
Authors: Öztürk, Şaban
Özkaya, Umut
Keywords: Colorectal cancer
Gastrointestinal tract
CNN
LSTM
Transfer learning
Issue Date: 2021
Publisher: ACADEMIC PRESS INC ELSEVIER SCIENCE
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.
URI: https://doi.org/10.1016/j.jbi.2020.103638
https://hdl.handle.net/20.500.13091/1168
ISSN: 1532-0464
1532-0480
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 SizeFormat 
1-s2.0-S1532046420302677-main.pdf
  Until 2030-01-01
5.25 MBAdobe PDFView/Open    Request a copy
Show full item record

CORE Recommender

SCOPUSTM   
Citations

17
checked on Feb 4, 2023

WEB OF SCIENCETM
Citations

20
checked on Jan 30, 2023

Page view(s)

48
checked on Jan 30, 2023

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.