Vol.68, No.2, 2021, pp.2041-2056, doi:10.32604/cmc.2021.015916
Gastric Tract Disease Recognition Using Optimized Deep Learning Features
  • Zainab Nayyar1, Muhammad Attique Khan1, Musaed Alhussein2, Muhammad Nazir1, Khursheed Aurangzeb2, Yunyoung Nam3,*, Seifedine Kadry4, Syed Irtaza Haider2
1 Department of Computer Science, HITEC University, Taxila, 47040, Pakistan
2 Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia
3 Department of Computer Science and Engineering, Soonchunhyang University, Asan, Korea
4 Department of Mathematics and Computer Science, Faculty of Science, Beirut Arab University, Beirut, Lebanon
* Corresponding Author: Yunyoung Nam. Email:
(This article belongs to this Special Issue: Artificial Intelligence and IoT based intelligent systems using high performance computing for Medical applications.)
Received 14 December 2020; Accepted 13 February 2021; Issue published 13 April 2021
Artificial intelligence aids for healthcare have received a great deal of attention. Approximately one million patients with gastrointestinal diseases have been diagnosed via wireless capsule endoscopy (WCE). Early diagnosis facilitates appropriate treatment and saves lives. Deep learning-based techniques have been used to identify gastrointestinal ulcers, bleeding sites, and polyps. However, small lesions may be misclassified. We developed a deep learning-based best-feature method to classify various stomach diseases evident in WCE images. Initially, we use hybrid contrast enhancement to distinguish diseased from normal regions. Then, a pretrained model is fine-tuned, and further training is done via transfer learning. Deep features are extracted from the last two layers and fused using a vector length-based approach. We improve the genetic algorithm using a fitness function and kurtosis to select optimal features that are graded by a classifier. We evaluate a database containing 24,000 WCE images of ulcers, bleeding sites, polyps, and healthy tissue. The cubic support vector machine classifier was optimal; the average accuracy was 99%.
Stomach cancer; contrast enhancement; deep learning; optimization; features fusion
Cite This Article
Z. Nayyar, M. A. Khan, M. Alhussein, M. Nazir, K. Aurangzeb et al., "Gastric tract disease recognition using optimized deep learning features," Computers, Materials & Continua, vol. 68, no.2, pp. 2041–2056, 2021.
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