Affiliation:
1. Department of Computer Science, HITEC University, Taxila 47080, Pakistan
2. Department of Software Engineering, Foundation University Islamabad, Islamabad 44000, Pakistan
3. Department of Software, Sejong University, Seoul 05006, Republic of Korea
4. Department of Computer Engineering, HITEC University, Taxila 47080, Pakistan
Abstract
Gastrointestinal (GI) tract diseases are on the rise in the world. These diseases can have fatal consequences if not diagnosed in the initial stages. WCE (wireless capsule endoscopy) is the advanced technology used to inspect gastrointestinal diseases such as ulcerative-colitis, polyps, esophagitis, and ulcers. WCE produces thousands of frames for a single patient’s procedure for which manual examination is tiresome, time-consuming, and prone to error; therefore, an automated procedure is needed. WCE images suffer from low contrast which increases inter-class and intra-class similarity and reduces the anticipated performance. In this paper, an efficient GI tract disease classification technique is proposed which utilizes an optimized brightness-controlled contrast-enhancement method to improve the contrast of the WCE images. The proposed technique applies a genetic algorithm (GA) for adjusting the values of contrast and brightness within an image by modifying the fitness function, which improves the overall quality of WCE images. This quality improvement is reported using qualitative measures, such as peak signal to noise ratio (PSNR), mean square error (MSE), visual information fidelity (VIF), similarity index (SI), and information quality index (IQI). As a second step, data augmentation is performed on WCE images by applying multiple transformations, and then, transfer learning is used to fine-tune a modified pre-trained model on WCE images. Finally, for the classification of GI tract disease, the extracted features are passed through multiple machine-learning classifiers. To show the efficacy of the proposed technique in the improvement in classification performance, the results are reported for the original dataset as well as the contrast-enhanced dataset. The results show an overall improvement of 15.26% in accuracy, 13.3% in precision, 16.77% in recall rate, and 15.18% in F-measure. Finally, a comparison with the existing techniques shows that the proposed framework outperforms the state-of-the-art techniques.
Funder
Ministry of Trade, Industry, and Energy (MOTIE) and the Korea Institute for Advancement of Technology (KIAT) through the International Cooperative R&D program.
Korean government
ITRC
IITP
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Cited by
8 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献