TY  - EJOU
AU  - Nayyar, Zainab 
AU  - Khan, Muhammad Attique 
AU  - Alhussein, Musaed 
AU  - Nazir, Muhammad 
AU  - Aurangzeb, Khursheed 
AU  - Nam, Yunyoung 
AU  - Kadry, Seifedine 
AU  - Haider, Syed Irtaza 

TI  - Gastric Tract Disease Recognition Using Optimized Deep Learning Features
T2  - Computers, Materials \& Continua

PY  - 2021
VL  - 68
IS  - 2
SN  - 1546-2226

AB  - 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%.
KW  - Stomach cancer; contrast enhancement; deep learning; optimization; features fusion

DO  - 10.32604/cmc.2021.015916