Wireless capsule endoscopy multiclass classification using three-dimensional deep convolutional neural network model

Mehrdokht Bordbar; Mohammad Sadegh Helfroush; Habibollah Danyali; Fardad Ejtehadi

doi:10.1186/s12938-023-01186-9

Wireless capsule endoscopy multiclass classification using three-dimensional deep convolutional neural network model

Biomed Eng Online. 2023 Dec 15;22(1):124. doi: 10.1186/s12938-023-01186-9.

Authors

Mehrdokht Bordbar¹, Mohammad Sadegh Helfroush², Habibollah Danyali¹, Fardad Ejtehadi³

Affiliations

¹ Department of Electrical Engineering, Shiraz University of Technology, Shiraz, Iran.
² Department of Electrical Engineering, Shiraz University of Technology, Shiraz, Iran. ms_helfroush@sutech.ac.ir.
³ Department of Internal Medicine, Gastroenterohepatology Research Center, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.

Abstract

Background: Wireless capsule endoscopy (WCE) is a patient-friendly and non-invasive technology that scans the whole of the gastrointestinal tract, including difficult-to-access regions like the small bowel. Major drawback of this technology is that the visual inspection of a large number of video frames produced during each examination makes the physician diagnosis process tedious and prone to error. Several computer-aided diagnosis (CAD) systems, such as deep network models, have been developed for the automatic recognition of abnormalities in WCE frames. Nevertheless, most of these studies have only focused on spatial information within individual WCE frames, missing the crucial temporal data within consecutive frames.

Methods: In this article, an automatic multiclass classification system based on a three-dimensional deep convolutional neural network (3D-CNN) is proposed, which utilizes the spatiotemporal information to facilitate the WCE diagnosis process. The 3D-CNN model fed with a series of sequential WCE frames in contrast to the two-dimensional (2D) model, which exploits frames as independent ones. Moreover, the proposed 3D deep model is compared with some pre-trained networks. The proposed models are trained and evaluated with 29 subject WCE videos (14,691 frames before augmentation). The performance advantages of 3D-CNN over 2D-CNN and pre-trained networks are verified in terms of sensitivity, specificity, and accuracy.

Results: 3D-CNN outperforms the 2D technique in all evaluation metrics (sensitivity: 98.92 vs. 98.05, specificity: 99.50 vs. 86.94, accuracy: 99.20 vs. 92.60). In conclusion, a novel 3D-CNN model for lesion detection in WCE frames is proposed in this study.

Conclusion: The results indicate the performance of 3D-CNN over 2D-CNN and some well-known pre-trained classifier networks. The proposed 3D-CNN model uses the rich temporal information in adjacent frames as well as spatial data to develop an accurate and efficient model.

Keywords: 3D convolutional neural network; Deep learning; Image classification; Wireless capsule endoscopy.

MeSH terms

Capsule Endoscopy* / methods
Diagnosis, Computer-Assisted
Humans
Neural Networks, Computer