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 diagn...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%.展开更多
基金supported by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(P0012724,The Competency Development Program for Industry Specialist)the Soonchunhyang University Research Fund.
文摘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%.
