A Robust Deep Model for Classification of Peptic Ulcer and Other Digestive Tract Disorders Using Endoscopic Images

Saqib Mahmood; Mian Muhammad Sadiq Fareed; Gulnaz Ahmed; Farhan Dawood; Shahid Zikria; Ahmad Mostafa; Syeda Fizzah Jilani; Muhammad Asad; Muhammad Aslam

doi:10.3390/biomedicines10092195

A Robust Deep Model for Classification of Peptic Ulcer and Other Digestive Tract Disorders Using Endoscopic Images

Biomedicines. 2022 Sep 5;10(9):2195. doi: 10.3390/biomedicines10092195.

Authors

Saqib Mahmood¹, Mian Muhammad Sadiq Fareed², Gulnaz Ahmed³, Farhan Dawood², Shahid Zikria¹, Ahmad Mostafa⁴, Syeda Fizzah Jilani⁵, Muhammad Asad⁶, Muhammad Aslam⁷

Affiliations

¹ Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan.
² Department of Software Engineering, University of Central Punjab, Lahore 54000, Pakistan.
³ The Institute of Artificial Intelligence and Marine Robots, Dalian Maritime University, Dalian 116026, China.
⁴ Department of Computer Science, Nagoya Institute of Technology, Nagoya 466-0061, Japan.
⁵ Department of Physics, Physical Sciences Building, Aberystwyth University, Aberystwyth SY23 3FL, UK.
⁶ Graduate School of Information Science and Technology, University of Tokyo, Tokyo 113-8656, Japan.
⁷ School of Computing Engineering and Physical Sciences, University of West of Scotland, Glasgow G72 0LH, UK.

Abstract

Accurate patient disease classification and detection through deep-learning (DL) models are increasingly contributing to the area of biomedical imaging. The most frequent gastrointestinal (GI) tract ailments are peptic ulcers and stomach cancer. Conventional endoscopy is a painful and hectic procedure for the patient while Wireless Capsule Endoscopy (WCE) is a useful technology for diagnosing GI problems and doing painless gut imaging. However, there is still a challenge to investigate thousands of images captured during the WCE procedure accurately and efficiently because existing deep models are not scored with significant accuracy on WCE image analysis. So, to prevent emergency conditions among patients, we need an efficient and accurate DL model for real-time analysis. In this study, we propose a reliable and efficient approach for classifying GI tract abnormalities using WCE images by applying a deep Convolutional Neural Network (CNN). For this purpose, we propose a custom CNN architecture named GI Disease-Detection Network (GIDD-Net) that is designed from scratch with relatively few parameters to detect GI tract disorders more accurately and efficiently at a low computational cost. Moreover, our model successfully distinguishes GI disorders by visualizing class activation patterns in the stomach bowls as a heat map. The Kvasir-Capsule image dataset has a significant class imbalance problem, we exploited a synthetic oversampling technique BORDERLINE SMOTE (BL-SMOTE) to evenly distribute the image among the classes to prevent the problem of class imbalance. The proposed model is evaluated against various metrics and achieved the following values for evaluation metrics: 98.9%, 99.8%, 98.9%, 98.9%, 98.8%, and 0.0474 for accuracy, AUC, F1-score, precision, recall, and loss, respectively. From the simulation results, it is noted that the proposed model outperforms other state-of-the-art models in all the evaluation metrics.

Keywords: BL-SMOTE; WCE dataset; class activation; computer-aided diagnosis; deep learning; image classification; imbalanced dataset; supervised learning.

Grants and funding

This work is supported and funded by TRL Technology Ltd.