Residual LSTM layered CNN for classification of gastrointestinal tract diseases

ÖZTÜRK Ş., Özkaya U.

Journal of Biomedical Informatics, vol.113, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 113
  • Publication Date: 2021
  • Doi Number: 10.1016/j.jbi.2020.103638
  • Journal Name: Journal of Biomedical Informatics
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, CINAHL, Communication Abstracts, Compendex, Computer & Applied Sciences, EMBASE, INSPEC, MEDLINE, Metadex, Veterinary Science Database, Civil Engineering Abstracts
  • Keywords: CNN, Colorectal cancer, Gastrointestinal tract, LSTM, Transfer learning
  • Ankara Haci Bayram Veli University Affiliated: Yes

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.