Classification of H. pylori Infection from Histopathological Images Using Deep Learning.
Helicobacter pylori (H. pylori) is a widespread pathogenic bacterium, impacting over 4 billion individuals globally. It is primarily linked to gastric diseases, including gastritis, peptic ulcers, and cancer. The current histopathological method for diagnosing H. pylori involves labour-intensive exa...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 3; pp. 1177 - 1187 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=178678173&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 178678173 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2024 vid: 37 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 178678173 178678173 178678173 10.1007/s10278-024-01021-0 178678173 ppf: 1177 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Classification of H. pylori Infection from Histopathological Images Using Deep Learning. aug: au: Ibrahim, Abdullahi Umar Dirilenoğlu, Fikret Hacisalihoğlu, Uğuray Payam Ilhan, Ahmet Mirzaei, Omid affil: https://ror.org/02x8svs93 Department of Biomedical Engineering, Faculty of Engineering, Near East University, Nicosia, Cyprus sug: subj: Helicobacter Infections Diagnosis Helicobacter Infections Classification Helicobacter Infections Pathology Deep Learning Methods Histological Techniques Human Models, Biological Sensitivity and Specificity Reproducibility of Results Prediction Models kappa Statistic Validation Studies ab: Helicobacter pylori (H. pylori) is a widespread pathogenic bacterium, impacting over 4 billion individuals globally. It is primarily linked to gastric diseases, including gastritis, peptic ulcers, and cancer. The current histopathological method for diagnosing H. pylori involves labour-intensive examination of endoscopic biopsies by trained pathologists. However, this process can be time-consuming and may occasionally result in the oversight of small bacterial quantities. Our study explored the potential of five pre-trained models for binary classification of 204 histopathological images, distinguishing between H. pylori-positive and H. pylori-negative cases. These models include EfficientNet-b0, DenseNet-201, ResNet-101, MobileNet-v2, and Xception. To evaluate the models' performance, we conducted a five-fold cross-validation, ensuring the models' reliability across different subsets of the dataset. After extensive evaluation and comparison of the models, ResNet101 emerged as the most promising. It achieved an average accuracy of 0.920, with impressive scores for sensitivity, specificity, positive predictive value, negative predictive value, F1 score, Matthews's correlation coefficient, and Cohen's kappa coefficient. Our study achieved these robust results using a smaller dataset compared to previous studies, highlighting the efficacy of deep learning models even with limited data. These findings underscore the potential of deep learning models, particularly ResNet101, to support pathologists in achieving precise and dependable diagnostic procedures for H. pylori. This is particularly valuable in scenarios where swift and accurate diagnoses are essential. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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