AI Article Synopsis
- Data quality issues are a major challenge in medical AI research, particularly in analyzing fundus images.
- The DeepFundus system uses deep learning to classify the quality of fundus images and offers real-time feedback for better image collection.
- The protocol for using DeepFundus is detailed through various steps and can be executed in Python with either a provided or custom dataset, as discussed in Liu et al.
Article Abstract
Data quality issues have been acknowledged as one of the greatest obstacles in medical artificial intelligence research. Here, we present DeepFundus, which employs deep learning techniques to perform multidimensional classification of fundus image quality and provide real-time guidance for on-site image acquisition. We describe steps for data preparation, model training, model inference, model evaluation, and the visualization of results using heatmaps. This protocol can be implemented in Python using either the suggested dataset or a customized dataset. For complete details on the use and execution of this protocol, please refer to Liu et al..
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10519839 | PMC |
| http://dx.doi.org/10.1016/j.xpro.2023.102565 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!