ArticleBiomedical optics express2021
Automatic classification of esophageal disease in gastroscopic images using an efficient channel attention deep dense convolutional neural network.
Article in Biomedical optics express, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Multimodal-multiscale hybrid fusion network for automated esophageal cancer diagnosis.iScience · 2026Article
- IDF-Net: Interpretable Dynamic Fusion Network for Colorectal Cancer Diagnosis Using Cross-Modal Imaging.Diagnostics (Basel, Switzerland) · 2025Article
- Application of deep learning-based convolutional neural networks in gastrointestinal disease endoscopic examination.World journal of gastroenterology · 2025Review
- Attention-Enhanced Multi-Task Deep Learning Model for Classification and Segmentation of Esophageal Lesions.ACS omega · 2025Article
- An Investigation into the Characteristics and Distribution Patterns of Diseases Detected via Gastroscopy in Northern Xinjiang, China.Journal of multidisciplinary healthcare · 2024Article
- High-Speed and Accurate Diagnosis of Gastrointestinal Disease: Learning on Endoscopy Images Using Lightweight Transformer with Local Feature Attention.Bioengineering (Basel, Switzerland) · 2023Article
- Application of convolutional neural network-based endoscopic imaging in esophageal cancer or high-grade dysplasia: A systematic review and meta-analysis.World journal of gastrointestinal oncology · 2023Article
- Lightweight deep learning model incorporating an attention mechanism and feature fusion for automatic classification of gastric lesions in gastroscopic images.Biomedical optics express · 2023Article
- A trial deep learning-based model for four-class histologic classification of colonic tumor from narrow band imaging.Scientific reports · 2023Article
- Semi-Supervised Segmentation Framework for Gastrointestinal Lesion Diagnosis in Endoscopic Images.Journal of personalized medicine · 2023Article
- Classification of Amino Acids Using Hybrid Terahertz Spectrum and an Efficient Channel Attention Convolutional Neural Network.Nanomaterials (Basel, Switzerland) · 2022Article
- Diagnosis of Esophageal Lesions by Multi-Classification and Segmentation Using an Improved Multi-Task Deep Learning Model.Sensors (Basel, Switzerland) · 2022Article
- Multi-Task Model for Esophageal Lesion Analysis Using Endoscopic Images: Classification with Image Retrieval and Segmentation with Attention.Sensors (Basel, Switzerland) · 2021Article
- Improving the Classification Performance of Esophageal Disease on Small Dataset by Semi-supervised Efficient Contrastive Learning.Journal of medical systems · 2021Article
- Polarization and Orbital Angular Momentum of Light in Biomedical Applications: feature issue introduction.Biomedical optics express · 2021Article
- Most favorable stimulation duration in the sensorimotor cortex for fNIRS-based BCI.Biomedical optics express · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The accurate diagnosis of various esophageal diseases at different stages is crucial for providing precision therapy planning and improving 5-year survival rate of esophageal cancer patients. Automatic classification of various esophageal diseases in gastroscopic images can assist doctors to improve the diagnosis efficiency and accuracy. The existing deep learning-based classification method can only classify very few categories of esophageal diseases at the same time. Hence, we proposed a novel efficient channel attention deep dense convolutional neural network (ECA-DDCNN), which can classify the esophageal gastroscopic images into four main categories including normal esophagus (NE), precancerous esophageal diseases (PEDs), early esophageal cancer (EEC) and advanced esophageal cancer (AEC), covering six common sub-categories of esophageal diseases and one normal esophagus (seven sub-categories). In total, 20,965 gastroscopic images were collected from 4,077 patients and used to train and test our proposed method. Extensive experiments results have demonstrated convincingly that our proposed ECA-DDCNN outperforms the other state-of-art methods. The classification accuracy (Acc) of our method is 90.63% and the averaged area under curve (AUC) is 0.9877. Compared with other state-of-art methods, our method shows better performance in the classification of various esophageal disease. Particularly for these esophageal diseases with similar mucosal features, our method also achieves higher true positive (TP) rates. In conclusion, our proposed classification method has confirmed its potential ability in a wide variety of esophageal disease diagnosis.
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