Summary of Exploring the Role Of Convolutional Neural Networks (cnn) in Dental Radiography Segmentation: a Comprehensive Systematic Literature Review, by Walid Brahmi and Imen Jdey and Fadoua Drira
Exploring the Role of Convolutional Neural Networks (CNN) in Dental Radiography Segmentation: A Comprehensive Systematic Literature Review
by Walid Brahmi, Imen Jdey, Fadoua Drira
First submitted to arxiv on: 17 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 explores the application of deep learning in dentistry, specifically focusing on advanced imaging techniques to improve diagnostic precision. The study highlights the potential of deep convolutional neural networks (CNNs) in analyzing images and detecting dental pathologies. By integrating cutting-edge technology, researchers aim to develop effective management strategies for dental conditions, which can have a significant impact on human health if left undetected. The paper also provides an overview of the current state of research in this area, standardizes the debate, and establishes baselines for future studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how computers can help doctors diagnose dental problems more accurately. Right now, dentists use X-rays and other imaging techniques to look for signs of problems like cavities or infections. But sometimes these images are hard to understand, which can lead to mistakes in diagnosis. The researchers wanted to see if a special type of computer program called a deep learning network could help solve this problem. They found that these networks can analyze images and detect dental pathologies very accurately. This has the potential to revolutionize how dentists diagnose and treat oral health issues. |
Keywords
* Artificial intelligence * Deep learning * Precision
