ArticleDiagnostics (Basel, Switzerland)2023
Detection of Gallbladder Disease Types Using Deep Learning: An Informative Medical Method.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 33 citations in OpenAlex.
- Review
- Deep Learning Approach for Biomedical Image Classification.Journal of imaging informatics in medicine · 2026Review
- A Deep Learning-Based Decision Support System for Cholelithiasis in MRI Data.Journal of clinical medicine · 2026Article
- Gallbladder disease diagnosis from ultrasound using squeeze-and-excitation capsule network with convolutional bidirectional long short-term memory.Scientific reports · 2026Article
- Gallstone disease classification using SLOA-optimized CatBoost classifier with explainable AI.PloS one · 2026Article
- GBCapsNet: A calibrated capsule network for automated gallbladder disease diagnosis via ultrasound imaging.PloS one · 2026Article
- Gallstone Classification Using Random Forest Optimized by Sand Cat Swarm Optimization Algorithm with SHAP and DiCE-Based Interpretability.Sensors (Basel, Switzerland) · 2025Article
- MSFE-GallNet-X: a multi-scale feature extraction-based CNN Model for gallbladder disease analysis with enhanced explainability.BMC medical imaging · 2025Article
- An end-to-end interpretable machine-learning-based framework for early-stage diagnosis of gallbladder cancer using multi-modality medical data.BMC cancer · 2025Article
- Clinical usefulness of nutritional and immunological indices to distinguish gallbladder carcinoma from benign disease.International journal of clinical oncology · 2025Article
- GBCHV an advanced deep learning anatomy aware model for accurate classification of gallbladder cancer utilizing ultrasound images.Scientific reports · 2025Article
- Detection of Gallbladder Disease Types Using a Feature Engineering-Based Developed CBIR System.Diagnostics (Basel, Switzerland) · 2025Article
- AI-assisted anatomical structure recognition and segmentation via mamba-transformer architecture in abdominal ultrasound images.Frontiers in artificial intelligence · 2025Article
- Transformer-powered precision: A DETR-based approach for robust detection in medical ultrasound with cholelithiasis as a case study.Computational and structural biotechnology journal · 2025Article
- Current status of artificial intelligence analysis for the diagnosis of gallbladder diseases using ultrasonography: a scoping review.Translational gastroenterology and hepatology · 2025Review
- Automated gall bladder cancer detection using artificial gorilla troops optimizer with transfer learning on ultrasound images.Scientific reports · 2024Article
- Recent Advances in Endoscopic Ultrasound for Gallbladder Disease Diagnosis.Diagnostics (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nowadays, despite all the conducted research and the provided efforts in advancing the healthcare sector, there is a strong need to rapidly and efficiently diagnose various diseases. The complexity of some disease mechanisms on one side and the dramatic life-saving potential on the other side raise big challenges for the development of tools for the early detection and diagnosis of diseases. Deep learning (DL), an area of artificial intelligence (AI), can be an informative medical tomography method that can aid in the early diagnosis of gallbladder (GB) disease based on ultrasound images (UI). Many researchers considered the classification of only one disease of the GB. In this work, we successfully managed to apply a deep neural network (DNN)-based classification model to a rich built database in order to detect nine diseases at once and to determine the type of disease using UI. In the first step, we built a balanced database composed of 10,692 UI of the GB organ from 1782 patients. These images were carefully collected from three hospitals over roughly three years and then classified by professionals. In the second step, we preprocessed and enhanced the dataset images in order to achieve the segmentation step. Finally, we applied and then compared four DNN models to analyze and classify these images in order to detect nine GB disease types. All the models produced good results in detecting GB diseases; the best was the MobileNet model, with an accuracy of 98.35%.
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