ArticleSensors (Basel, Switzerland)2023
Endoscopic Image Classification Based on Explainable Deep Learning.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed.
- Swarm Intelligence-Guided Hybrid Transfer Learning for Gastrointestinal Polyp Classification.Biomimetics (Basel, Switzerland) · 2026Article
- Gastrointestinal endoscopic image classification using a hybrid modified inception network and a customized vision transformer with tree growth feature selection.Scientific reports · 2026Article
- Artificial Intelligence in Gastrointestinal Endoscopy and Hemostatic Decision-Making: Current Evidence, Clinical Implications and Implementation Barriers.Life (Basel, Switzerland) · 2026Review
- Explainable multimodal knee osteoarthritis diagnosis from X-ray images using anomaly detection with clinical and structural biomarkers.Scientific reports · 2026Article
- Deep Learning Approach for Biomedical Image Classification.Journal of imaging informatics in medicine · 2026Review
- A hybrid ViT-L/32-MaxViT-L architecture with adaptive gated fusion for multiclass gastrointestinal disease detection and multi-methodFrontiers in endocrinology · 2026Article
- HMC-net: a ResNet fused hierarchical multi-scale cross-attention architecture for mammographic breast malignancy recognition incorporating explainable AI.Frontiers in oncology · 2026Article
- Artificial intelligence and robotic technologies redefining precision and personalization in orthopedic surgery: a narrative review.Frontiers in bioengineering and biotechnology · 2026Review
- Application of Explainable Artificial Intelligence Based on Visual Explanation in Digestive Endoscopy.Bioengineering (Basel, Switzerland) · 2025Review
- Computer-Aided Diagnosis of Equine Pharyngeal Lymphoid Hyperplasia Using the Object Detection-Based Processing Technique of Digital Endoscopic Images.Animals : an open access journal from MDPI · 2025Article
- Artificial Intelligence in Orthopedic Surgery: Current Applications, Challenges, and Future Directions.MedComm · 2025Review
- Enhanced Detection of Colon Diseases via a Fused Deep Learning Model with an Auxiliary Fusion Layer and Residual Blocks on Endoscopic Images.Current medical imaging · 2025Article
- CNN-Based Kidney Segmentation Using a Modified CLAHE Algorithm.Sensors (Basel, Switzerland) · 2024Article
- Spatial-attention ConvMixer architecture for classification and detection of gastrointestinal diseases using the Kvasir dataset.Health information science and systems · 2024Article
- A Review of Application of Deep Learning in Endoscopic Image Processing.Journal of imaging · 2024Review
- Efficient-gastro: optimized EfficientNet model for the detection of gastrointestinal disorders using transfer learning and wireless capsule endoscopy images.PeerJ. Computer science · 2024Article
- Development of an ensemble CNN model with explainable AI for the classification of gastrointestinal cancer.PloS one · 2024Article
- A Systematic Review of Real-Time Deep Learning Methods for Image-Based Cancer Diagnostics.Journal of multidisciplinary healthcare · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Deep learning has achieved remarkably positive results and impacts on medical diagnostics in recent years. Due to its use in several proposals, deep learning has reached sufficient accuracy to implement; however, the algorithms are black boxes that are hard to understand, and model decisions are often made without reason or explanation. To reduce this gap, explainable artificial intelligence (XAI) offers a huge opportunity to receive informed decision support from deep learning models and opens the black box of the method. We conducted an explainable deep learning method based on ResNet152 combined with Grad-CAM for endoscopy image classification. We used an open-source KVASIR dataset that consisted of a total of 8000 wireless capsule images. The heat map of the classification results and an efficient augmentation method achieved a high positive result with 98.28% training and 93.46% validation accuracy in terms of medical image classification.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.