Reham Faeq Hasona
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Reham Faeq Hasona has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Eman M. El-Gendy
Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
1 shared publication - Mahmoud M. Saafan
Computers Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
1 shared publication - Wafaa Farouk Mohamed
Ain Shams University Specialized Hospital, Ain Shams University, Cairo 11566, Egypt
1 shared publication - Walaa Ghanam
Pathology Department, Faculty of Medicine, Suez University, Suez 43533, Egypt
1 shared publication
Latest Publications
- Open Access
- 1 October 2026
- Published by MDPI AG in Microorganisms
- Vol. 14 (10) , 2207
Bayesian-Optimized Deep Learning for Helicobacter pylori Detection in Egyptian Gastric Histopathology: A Multi-Architecture Study
Helicobacterpylori (H. pylori) infection is a significant health issue. It is classified by the World Health Organization as a Group I carcinogen. Egypt has unusually high prevalence rates of 64.6–90.3%, significantly above the global averages. Histopathological diagnosis is tedious and time-consuming and has high interobserver variation, especially with small, scattered bacterial colonies. This study represents the first comprehensive application of artificial intelligence to this diagnostic task. We compared eight state-of-the-art architectures. The models were trained for 50 epochs and assessed on 100 histopathological whole-slide images using metrics including accuracy, sensitivity, specificity, and AUC-ROC. MobileNetV2 and Xception performed perfectly with 100% accuracy, sensitivity, and specificity, an AUC of 1.0, and zero misclassifications. MobileNetV2 was trained 2.3 times faster, offering an efficiency advantage relevant to deployment in resource-limited pathology laboratories, pending external validation. Another four models (DenseNet-201, InceptionV3, VGG-16, and VGG-19) recorded 100% test accuracy, while Vision Transformer recorded 96.97%. These results represent retrospective patch-level classification performance and do not constitute clinical diagnostic validation. This study not only establishes the first validated artificial intelligence classification system of H. pylori in Egypt but also shows high performance on Egyptian samples and offers a solid methodological groundwork for future medical AI applications in resource-constrained contexts.
- 1 October 2026
- Published by MDPI AG in Microorganisms
- Vol. 14 (10) , 2207
Open AccessBayesian-Optimized Deep Learning for Helicobacter pylori Detection in Egyptian Gastric Histopathology: A Multi-Architecture Study
Helicobacterpylori (H. pylori) infection is a significant health issue. It is classified by the World Health Organization as a Group I carcinogen. Egypt has unusually high prevalence rates of 64.6–90.3%, significantly above the global averages. Histopathological diagnosis is tedious and time-consuming and has high interobserver variation, especially with small, scattered bacterial colonies. This study represents the first comprehensive application of artificial intelligence to this diagnostic task. We compared eight state-of-the-art architectures. The models were trained for 50 epochs and assessed on 100 histopathological whole-slide images using metrics including accuracy, sensitivity, specificity, and AUC-ROC. MobileNetV2 and Xception performed perfectly with 100% accuracy, sensitivity, and specificity, an AUC of 1.0, and zero misclassifications. MobileNetV2 was trained 2.3 times faster, offering an efficiency advantage relevant to deployment in resource-limited pathology laboratories, pending external validation. Another four models (DenseNet-201, InceptionV3, VGG-16, and VGG-19) recorded 100% test accuracy, while Vision Transformer recorded 96.97%. These results represent retrospective patch-level classification performance and do not constitute clinical diagnostic validation. This study not only establishes the first validated artificial intelligence classification system of H. pylori in Egypt but also shows high performance on Egyptian samples and offers a solid methodological groundwork for future medical AI applications in resource-constrained contexts.