@Article{cmc.2021.015916,
AUTHOR = {Zainab Nayyar, Muhammad Attique Khan, Musaed Alhussein, Muhammad Nazir, Khursheed Aurangzeb, Yunyoung Nam, Seifedine Kadry, Syed Irtaza Haider},
TITLE = {Gastric Tract Disease Recognition Using Optimized Deep Learning Features},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {68},
YEAR = {2021},
NUMBER = {2},
PAGES = {2041--2056},
URL = {http://www.techscience.com/cmc/v68n2/42162},
ISSN = {1546-2226},
ABSTRACT = {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%.},
DOI = {10.32604/cmc.2021.015916}
}