Gastrointestinal Tract Disease Classification from Wireless Endoscopy Images Using Pretrained Deep Learning Model
| Autor: | P. Anitha, P. Jenopaul, M. G. Sumithra, C. Suresh Gnana Dhas, J. Yogapriya, V. Chandran |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Databases
Factual Article Subject Gastrointestinal Diseases Computer science Computer applications to medicine. Medical informatics R858-859.7 Image processing Capsule Endoscopy Convolutional neural network General Biochemistry Genetics and Molecular Biology law.invention Deep Learning Capsule endoscopy law Image Interpretation Computer-Assisted Digital image processing Humans Wireless General Immunology and Microbiology Norway business.industry Applied Mathematics Deep learning Computational Biology Pattern recognition General Medicine Matthews correlation coefficient Gastrointestinal Tract Modeling and Simulation Imaging technology Neural Networks Computer Artificial intelligence business Wireless Technology Algorithms Research Article |
| Zdroj: | Computational and Mathematical Methods in Medicine, Vol 2021 (2021) Computational and Mathematical Methods in Medicine |
| ISSN: | 1748-670X |
| DOI: | 10.1155/2021/5940433 |
| Popis: | Wireless capsule endoscopy is a noninvasive wireless imaging technology that becomes increasingly popular in recent years. One of the major drawbacks of this technology is that it generates a large number of photos that must be analyzed by medical personnel, which takes time. Various research groups have proposed different image processing and machine learning techniques to classify gastrointestinal tract diseases in recent years. Traditional image processing algorithms and a data augmentation technique are combined with an adjusted pretrained deep convolutional neural network to classify diseases in the gastrointestinal tract from wireless endoscopy images in this research. We take advantage of pretrained models VGG16, ResNet-18, and GoogLeNet, a convolutional neural network (CNN) model with adjusted fully connected and output layers. The proposed models are validated with a dataset consisting of 6702 images of 8 classes. The VGG16 model achieved the highest results with 96.33% accuracy, 96.37% recall, 96.5% precision, and 96.5% F1-measure. Compared to other state-of-the-art models, the VGG16 model has the highest Matthews Correlation Coefficient value of 0.95 and Cohen’s kappa score of 0.96. |
| Databáze: | OpenAIRE |
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