Journal ArticleDOI
Identification of lesion images from gastrointestinal endoscope based on feature extraction of combinational methods with and without learning process
Dingyun Liu,Tao Gan,Nini Rao,Yao-Wen Xing,Jie Zheng,Sang Li,Chengsi Luo,Zhong-Jun Zhou,Wan Yongli +8 more
TLDR
A new computer-aided method to detect lesion images and provide worthwhile guidance for improving the efficiency and accuracy of gastrointestinal disease diagnosis and is a good prospect for clinical application.About:
This article is published in Medical Image Analysis.The article was published on 2016-08-01. It has received 53 citations till now. The article focuses on the topics: Feature extraction.read more
Citations
More filters
Journal ArticleDOI
Application of Artificial Intelligence to Gastroenterology and Hepatology
Catherine Le Berre,William J. Sandborn,Sabeur Aridhi,Marie-Dominique Devignes,Laure Fournier,Malika Smaïl-Tabbone,Silvio Danese,Laurent Peyrin-Biroulet +7 more
TL;DR: The ways in which AI may help physicians make a diagnosis or establish a prognosis are reviewed and its limitations are discussed, knowing that further randomized controlled studies will be required before the approval of AI techniques by the health authorities.
Journal ArticleDOI
The combination of circulating long noncoding RNAs AK001058, INHBA-AS1, MIR4435-2HG, and CEBPA-AS1 fragments in plasma serve as diagnostic markers for gastric cancer.
TL;DR: The results demonstrate that certain lncRNAs, such as AK001058, INHBA-as1, MIR4435-2HG, and CEBPA-AS1, are enriched in human gastric cancer tissues and significantly elevated in the plasma of patients with Gastric cancer.
Journal ArticleDOI
Review on the Applications of Deep Learning in the Analysis of Gastrointestinal Endoscopy Images
TL;DR: This review summarized and compared the latest published literature related to the common clinical GI diseases and covers the key applications of DL in GI image detection, classification, segmentation, recognition, location, and other tasks.
Journal ArticleDOI
Artificial intelligence in gastric cancer: Application and future perspectives
TL;DR: The current status of AI-applications was systematically summarized in gastric cancer and future directions that target this field were analyzed to overcome the risk of overfitting AI models and enhance their accuracy as well as the applicability in clinical practice.
Journal ArticleDOI
Comparative assessment of CNN architectures for classification of breast FNAC images.
TL;DR: A comparative assessment of the models giving a new dimension to FNAC study where GoogLeNet-V3 (fine-tuned) achieved an accuracy of 96.25% which is highly satisfactory.
References
More filters
Journal ArticleDOI
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Journal ArticleDOI
Eigenfaces for recognition
Matthew Turk,Alex Pentland +1 more
TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
Journal ArticleDOI
Medical image analysis: progress over two decades and the challenges ahead
James S. Duncan,Nicholas Ayache +1 more
TL;DR: A look at progress in the field over the last 20 years is looked at and some of the challenges that remain for the years to come are suggested.
Proceedings ArticleDOI
Comparing images using color coherence vectors
TL;DR: It is shown that CCV’s can give superior results to color histogram-based methods for comparing images that incorporates spatial information, and to whom correspondence should be addressed tograms for image retrieval.
Journal ArticleDOI
Computer-aided tumor detection in endoscopic video using color wavelet features
TL;DR: An approach to the detection of tumors in colonoscopic video based on a new color feature extraction scheme to represent the different regions in the frame sequence based on the wavelet decomposition, reaching 97% specificity and 90% sensitivity.