Academic Journal
Comparative study of convolutional neural network architectures for gastrointestinal lesions classification
| العنوان: | Comparative study of convolutional neural network architectures for gastrointestinal lesions classification |
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| المؤلفون: | Erik O. Cuevas-Rodriguez, Carlos E. Galvan-Tejada, Valeria Maeda-Gutiérrez, Gamaliel Moreno-Chávez, Jorge I. Galván-Tejada, Hamurabi Gamboa-Rosales, Huizilopoztli Luna-García, Arturo Moreno-Baez, José María Celaya-Padilla |
| المصدر: | PeerJ, Vol 11, p e14806 (2023) |
| بيانات النشر: | PeerJ Inc. |
| سنة النشر: | 2023 |
| المجموعة: | Directory of Open Access Journals: DOAJ Articles |
| مصطلحات موضوعية: | Convolutional neural network, Gastrointestinal lesions, Classification, Deep learning, Endoscopy, Gastrointestinal, Medicine, Biology (General), QH301-705.5 |
| الوصف: | The gastrointestinal (GI) tract can be affected by different diseases or lesions such as esophagitis, ulcers, hemorrhoids, and polyps, among others. Some of them can be precursors of cancer such as polyps. Endoscopy is the standard procedure for the detection of these lesions. The main drawback of this procedure is that the diagnosis depends on the expertise of the doctor. This means that some important findings may be missed. In recent years, this problem has been addressed by deep learning (DL) techniques. Endoscopic studies use digital images. The most widely used DL technique for image processing is the convolutional neural network (CNN) due to its high accuracy for modeling complex phenomena. There are different CNNs that are characterized by their architecture. In this article, four architectures are compared: AlexNet, DenseNet-201, Inception-v3, and ResNet-101. To determine which architecture best classifies GI tract lesions, a set of metrics; accuracy, precision, sensitivity, specificity, F1-score, and area under the curve (AUC) were used. These architectures were trained and tested on the HyperKvasir dataset. From this dataset, a total of 6,792 images corresponding to 10 findings were used. A transfer learning approach and a data augmentation technique were applied. The best performing architecture was DenseNet-201, whose results were: 97.11% of accuracy, 96.3% sensitivity, 99.67% specificity, and 95% AUC. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 2167-8359 |
| Relation: | https://peerj.com/articles/14806.pdf; https://peerj.com/articles/14806/; https://doaj.org/toc/2167-8359; https://doaj.org/article/8ae2e7b6ee5b4a3ab3023c3030755245 |
| DOI: | 10.7717/peerj.14806 |
| الاتاحة: | https://doi.org/10.7717/peerj.14806 https://doaj.org/article/8ae2e7b6ee5b4a3ab3023c3030755245 |
| رقم الانضمام: | edsbas.1CC91610 |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 21678359 |
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| DOI: | 10.7717/peerj.14806 |