Summary of Case-based Reasoning Approach For Diagnostic Screening Of Children with Developmental Delays, by Zichen Song et al.
Case-based reasoning approach for diagnostic screening of children with developmental delays
by Zichen Song, Jiakang Li, Songning Lai, Sitan Huang
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a hybrid model combining a Convolutional Neural Network (CNN)-Transformer model with Case-Based Reasoning (CBR) to enhance screening efficiency for children with developmental delays. The CNN-Transformer model is effective in identifying features in bone age images, while CBR’s memory capability enables comparison of new cases based on previously stored old cases. The study aims to establish a screening system for developmental delays and improve screening efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research uses artificial intelligence to help identify children with developmental delays. It combines two techniques: one that looks at pictures of bones (CNN) and another that learns from past experiences (CBR). This combination helps find features in bone age images and compares new cases to old ones to make predictions. The goal is to create a system that can quickly and accurately diagnose developmental delays, making it easier to provide early intervention. |
Keywords
» Artificial intelligence » Cnn » Neural network » Transformer
