MSFE-GallNet-X: a multi-scale feature extraction-based CNN Model for gallbladder disease analysis with enhanced explainability
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| Publicat a: | BMC Medical Imaging vol. 25 (2025), p. 1-22 |
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| Autor principal: | |
| Altres autors: | , , , |
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Springer Nature B.V.
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| Accés en línia: | Citation/Abstract Full Text Full Text - PDF |
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| 001 | 3247110499 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1471-2342 | ||
| 024 | 7 | |a 10.1186/s12880-025-01902-y |2 doi | |
| 035 | |a 3247110499 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 58449 |2 nlm | ||
| 100 | 1 | |a Hadiur Rahman Nabil | |
| 245 | 1 | |a MSFE-GallNet-X: a multi-scale feature extraction-based CNN Model for gallbladder disease analysis with enhanced explainability | |
| 260 | |b Springer Nature B.V. |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Section ObjectiveThis study introduces MSFE-GallNet-X, a domain-adaptive deep learning model utilizing multi-scale feature extraction (MSFE) to improve the classification accuracy of gallbladder diseases from grayscale ultrasound images, while integrating explainable artificial intelligence (XAI) methods to enhance clinical interpretability.AbstractSection MethodsWe developed a convolutional neural network-based architecture that automatically learns multi-scale features from a dataset comprising 10,692 high-resolution ultrasound images from 1,782 patients, covering nine gallbladder disease classes, including gallstones, cholecystitis, and carcinoma. The model incorporated Gradient-Weighted Class Activation Mapping (Grad-CAM) and Local Interpretable Model-Agnostic Explanations (LIME) to provide visual interpretability of diagnostic predictions. Model performance was evaluated using standard metrics, including accuracy and F1 score.AbstractSection ResultsThe MSFE-GallNet-X achieved a classification accuracy of 99.63% and an F1 score of 99.50%, outperforming state-of-the-art models including VGG-19 (98.89%) and DenseNet121 (91.81%), while maintaining greater parameter efficiency, only 1·91 M parameters in gallbladder disease classification. Visualization through Grad-CAM and LIME highlighted critical image regions influencing model predictions, supporting explainability for clinical use.AbstractSection ConclusionMSFE-GallNet-X demonstrates strong performance on a controlled and balanced dataset, suggesting its potential as an AI-assisted tool for clinical decision-making in gallbladder disease management.AbstractSection Clinical trial numberNot applicable. | |
| 653 | |a Feature extraction | ||
| 653 | |a Accuracy | ||
| 653 | |a Gallbladder | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Datasets | ||
| 653 | |a Deep learning | ||
| 653 | |a Performance evaluation | ||
| 653 | |a Classification | ||
| 653 | |a Image resolution | ||
| 653 | |a Disease | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Gallbladder cancer | ||
| 653 | |a Medical imaging | ||
| 653 | |a Gallbladder diseases | ||
| 653 | |a Gallstones | ||
| 653 | |a Abdomen | ||
| 653 | |a Machine learning | ||
| 653 | |a Explainable artificial intelligence | ||
| 653 | |a Breast cancer | ||
| 653 | |a Efficiency | ||
| 653 | |a Calculi | ||
| 653 | |a Ultrasound | ||
| 653 | |a Clinical decision making | ||
| 653 | |a Cholecystitis | ||
| 653 | |a Parameters | ||
| 653 | |a Ultrasonic imaging | ||
| 653 | |a Decision making | ||
| 653 | |a Neural networks | ||
| 700 | 1 | |a Istyak Ahmed | |
| 700 | 1 | |a Das, Aritra | |
| 700 | 1 | |a M. F. Mridha Mohsin Kabir | |
| 700 | 1 | |a Aung, Zeyar | |
| 773 | 0 | |t BMC Medical Imaging |g vol. 25 (2025), p. 1-22 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3247110499/abstract/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3247110499/fulltext/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3247110499/fulltextPDF/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |