Summary of Research on Intelligent Aided Diagnosis System Of Medical Image Based on Computer Deep Learning, by Jiajie Yuan et al.
Research on Intelligent Aided Diagnosis System of Medical Image Based on Computer Deep Learning
by Jiajie Yuan, Linxiao Wu, Yulu Gong, Zhou Yu, Ziang Liu, Shuyao He
First submitted to arxiv on: 29 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper combines Struts and Hibernate architectures using DAO to store and access data, establishing a dual-mode humidity medical image library suitable for deep networks. A dual-mode assisted diagnosis method based on images is proposed, achieving an optimal operating characteristic under curve product (AUROC) of 0.9985, recall rate of 0.9814, and accuracy of 0.9833 through various feature extraction methods. This practical method can be applied to clinical diagnosis, allowing outpatient doctors to quickly register or log in to the platform for image uploading and obtaining more accurate diagnoses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper combines two architectures, Struts and Hibernate, using DAO to store and access data. It creates a special library of medical images that can be used with deep networks. The researchers also propose a new way to diagnose patients using these images. They tested their method and it worked really well, achieving high accuracy rates. This means that doctors can use the system to quickly get more accurate diagnoses for their patients. |
Keywords
* Artificial intelligence * Feature extraction * Recall
