Detection of Helicobacter pylori Infection in Histopathological Gastric Biopsies Using Deep Learning Models
Abstract
1. Introduction
Related Work
2. Materials and Methods
2.1. Dataset and Annotations
2.2. Immunohistochemical Staining
2.3. Image Pre-Processing
2.4. Dataset Filtering and Quality Assurance
2.5. Data Augmentation and Class Balancing
2.6. Deep Convolutional Neural Network Models
2.7. Validation and Interpretability with GradCAM
2.8. Experimental Design
2.9. Statistical Analysis
3. Results
3.1. Dataset Curation and Augmentation Strategy
3.2. Model Performance
3.3. External Validation
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Phase | Patients | Biopsies | Patches Included | Notes |
---|---|---|---|---|
Training | 16 (10 HP+/6HP−) | 80 (48 HP+/32HP−) | Yes | Used for model fitting and augmentation |
Test | 4 (2 HP+/2HP−) | 20 (12 HP+/8HP−) | Yes | Held-out internal test set |
DL Model | Accuracy | Precision | Recall | Specificity | F1 Score | MCC |
---|---|---|---|---|---|---|
InceptionV3 | 98% | 97% | 100% | 97% | 98% | 97% |
VGG16 | 98% | 97% | 100% | 97% | 98% | 96% |
ResNet50 | 97% | 97% | 99% | 96% | 98% | 95% |
BoostedNet | 85% | 87% | 84% | 87% | 86% | 82% |
AutoKeras | 89% | 92% | 85% | 93% | 88% | 78% |
DL Model | Accuracy | Precision | Recall | Specificity | F1 Score | MCC |
---|---|---|---|---|---|---|
InceptionV3 | 97% | 94% | 100% | 94% | 97% | 93% |
VGG16 | 96% | 94% | 98% | 94% | 96% | 92% |
ResNet50 | 96% | 94% | 97% | 94% | 96% | 91% |
BoostedNet | 83% | 84% | 83% | 84% | 84% | 68% |
AutoKeras | 82% | 85% | 80% | 84% | 82% | 64% |
Authors and Year (Ref) | Databases | Validation Stain | AUC (IC 95%) | DL Architecture | Additional Pre-Processing | xAI | Metadata | Total Number of WSIs | Patch Size (Pixel) | Training Set (WSIs) | Validation Set (WSIs) | Test Set (WSIs) | External Validation |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Present article | Institutional | H&E | 1.0 (N/A) | InceptionV3 | Quality filtering: patches containing more than 60% background or minimal/no visible tissue were discarded. Data augmentation (rotation, horizontal flip, zoom, and width and height shift) | No | Yes | 100 | 512 × 512 | 80 | N/A | 20 | Yes |
1.0 (N/A) | VGG16 | ||||||||||||
1.0 (N/A) | ResNet50 | ||||||||||||
0.92 (N/A) | BoostedNet | ||||||||||||
0.92 (N/A) | AutoKeras | ||||||||||||
Cano, et al., 2025 [17] | Institutional | IHC | 0.961 (N/A) | Autoencoder | Morphological operations, conversion to HSV and pixel filtering, sliding windows on the edges | No | No | 245 | 256 × 256 | 123 | N/A | 122 | No |
0.77 (N/A) | ResNet18 | ||||||||||||
0.92 (N/A) | ResTreshold | ||||||||||||
0.88 (N/A) | UNI Vit | ||||||||||||
Krishna, et al., 2024 [15] | Public dataset | H&E, Giemsa | 0.990 (N/A) | BoostedNet: CNN + XGBoost | Resize to 256 × 256, Gaussian filter, data augmentation (rotation, zoom, shear, flip) | Yes | No | 19 | 256 × 256 | N/A | N/A | N/A | Yes |
Ibrahim, et al., 2024 [13] | Institutional | H&E | 0.941 (N/A) | ResNet-101 | N/A | No | No | 204 | 960 × 1280 | CV | CV | CV | No |
0.930 (N/A) | DenseNet-201 | ||||||||||||
0.903 (N/A) | MobileNet-v2 | ||||||||||||
0.917 (N/A) | InceptionV3 | ||||||||||||
0.907 (N/A) | Xception | ||||||||||||
Lin, et al., 2023 [16] | Institutional | H&E | 0.973 (0.954–0.993) | ESCNN + Logistic Regression | N/A | Yes | No | 1075 | N/A | 885 | N/A | 190 | Yes |
Franklin, et al., 2022 [27] | Institutional | H&E | N/A | HALO-AI software (fully convolutional VGG network) | Data augmentation: rotations variations in hue, saturation, contrast, and brightness | No | Yes | 187 | 400 × 400 | 112 | N/A | 75 | No |
Liscia, et al., 2022 [11] | Institutional | W-S | 0.938 (N/A) | CNN-based model via Microsoft Custom Vision (VGG-based model) | NDPI to TIFF conversion | No | Yes | 185 | 2000 × 2000 | N/A | N/A | N/A | No |
Gonçalves, et al., 2022 [9] | Institutional | H&E | 0.998 | VGG16 | Noise correction, grayscale, binarization, augmentation (rotation, flip, zoom) | No | No | 19 | 256 × 256 | N/A | N/A | N/A | No |
0.994 | InceptionV3 | ||||||||||||
0.994 | ResNet50 | ||||||||||||
Martin, et al., 2020 [12] | Institutional | H&E | 1.00 (N/A) | HALO-AI software (fully convolutional VGG network) | Data augmentation with random rotations and random changes in hue, saturation, contrast, and brightness | No | Si | 300 | 400 × 400 | 210 | 90 | N/A | Yes |
Klein, et al., 2020 [10] | Institutional | Giemsa | 0.950 (N/A) | Compact VGG-style deep neural network | Data augmentation, Rgb to Hsv conversion, Otsu’s thresholding, morphological operations, contour detection | Yes | Yes | 627 | 224 × 224 | 477 | 150 | N/A | Yes |
0.902 (N/A) | |||||||||||||
0.810 (N/A) | |||||||||||||
Zhou, et al., 2020 [14] | Institutional | H&E | 0.965 (0.934–0.987) | MobileNet-V2 | Laplacian filtering, data augmentation through horizontal inversion | Yes | Yes | 108 | 299 × 299 | 77 | 31 | N/A | No |
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Parra-Medina, R.; Zambrano-Betancourt, C.; Peña-Rojas, S.; Quintero-Ortiz, L.; Caro, M.V.; Romero, I.; Gil-Gómez, J.H.; Sprockel, J.J.; Cancino, S.; Mosquera-Zamudio, A. Detection of Helicobacter pylori Infection in Histopathological Gastric Biopsies Using Deep Learning Models. J. Imaging 2025, 11, 226. https://doi.org/10.3390/jimaging11070226
Parra-Medina R, Zambrano-Betancourt C, Peña-Rojas S, Quintero-Ortiz L, Caro MV, Romero I, Gil-Gómez JH, Sprockel JJ, Cancino S, Mosquera-Zamudio A. Detection of Helicobacter pylori Infection in Histopathological Gastric Biopsies Using Deep Learning Models. Journal of Imaging. 2025; 11(7):226. https://doi.org/10.3390/jimaging11070226
Chicago/Turabian StyleParra-Medina, Rafael, Carlos Zambrano-Betancourt, Sergio Peña-Rojas, Lina Quintero-Ortiz, Maria Victoria Caro, Ivan Romero, Javier Hernand Gil-Gómez, John Jaime Sprockel, Sandra Cancino, and Andres Mosquera-Zamudio. 2025. "Detection of Helicobacter pylori Infection in Histopathological Gastric Biopsies Using Deep Learning Models" Journal of Imaging 11, no. 7: 226. https://doi.org/10.3390/jimaging11070226
APA StyleParra-Medina, R., Zambrano-Betancourt, C., Peña-Rojas, S., Quintero-Ortiz, L., Caro, M. V., Romero, I., Gil-Gómez, J. H., Sprockel, J. J., Cancino, S., & Mosquera-Zamudio, A. (2025). Detection of Helicobacter pylori Infection in Histopathological Gastric Biopsies Using Deep Learning Models. Journal of Imaging, 11(7), 226. https://doi.org/10.3390/jimaging11070226