Summary of Machine Learning Approach to Brain Tumor Detection and Classification, by Alice Oh et al.
Machine learning approach to brain tumor detection and classification
by Alice Oh, Inyoung Noh, Jian Choo, Jihoo Lee, Justin Park, Kate Hwang, Sanghyeon Kim, Soo Min Oh
First submitted to arxiv on: 16 Oct 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 explores the application of various statistical and machine learning models to detect and classify brain tumors using magnetic resonance imaging (MRI) images. The authors investigate a range of approaches including linear, logistic, and Bayesian regressions, as well as decision trees, random forests, single-layer perceptrons, multi-layer perceptrons, convolutional neural networks (CNNs), recurrent neural networks, and long short-term memory models. The results demonstrate that CNN-based models outperform other approaches, achieving the best performance for brain tumor detection and classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses computer programs to help doctors find and identify different types of brain tumors from MRI pictures. It looks at many different ways to do this, including some very advanced math techniques. The main finding is that one type of computer program called a CNN works the best. This means that with more testing, these computer programs could be used to help doctors make accurate diagnoses earlier on. |
Keywords
* Artificial intelligence * Classification * Cnn * Machine learning
