Summary of Comparative Analysis Of Machine Learning Approaches For Bone Age Assessment: a Comprehensive Study on Three Distinct Models, by Nandavardhan R. et al.
Comparative Analysis of Machine Learning Approaches for Bone Age Assessment: A Comprehensive Study on Three Distinct Models
by Nandavardhan R., Somanathan R., Vikram Suresh, Savaridassan P
First submitted to arxiv on: 15 Nov 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 The paper presents a study on automating bone age prediction from X-ray images of non-dominant hands in children and infants using machine learning models. This task is crucial for assessing the possibility of genetic conditions and growth abnormalities, but current conventional methods like The Greulich Pyle (GP) or Tanner Whitehouse (TW) approach rely on human expertise and may lead to observer bias. To overcome these limitations, several machine learning models have been developed with varying levels of accuracy and efficiency. This study compares the performance of three widely used models – Xception, VGG, and CNN – in terms of mean absolute error (MAE) in months. The findings provide insights into the strengths and weaknesses of each model, enabling a more informed choice for practitioners. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using special computers to help doctors look at pictures of children’s hands taken with X-rays. Doctors use these pictures to figure out if kids are growing normally or if there might be some problem. Right now, doctors do this by looking at the pictures themselves and comparing them to what they know about how kids grow. But this can be tricky and sometimes doctors might not agree on what they see. To make it easier, some smart computers have been built that can look at the pictures too. This study compares three of these computers – Xception, VGG, and CNN – to see which one is best. |
Keywords
» Artificial intelligence » Cnn » Machine learning » Mae
