Summary of Automated Bi-fold Weighted Ensemble Algorithms and Its Application to Brain Tumor Detection and Classification, by Potsang B. Huang et al.
Automated Bi-Fold Weighted Ensemble Algorithms and its Application to Brain Tumor Detection and Classification
by PoTsang B. Huang, Muhammad Rizwan, Mehboob Ali
First submitted to arxiv on: 31 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 tackles brain tumors, which have high mortality rates due to limited diagnostic capabilities, particularly in developing countries. Early diagnosis is crucial for effective management and reducing mortality rates. However, current diagnostic methods are hindered by limitations such as high costs and lengthy result acquisition times, impeding early detection. The study presents two novel bi-fold weighted voting ensemble models that aim to improve the effectiveness of weighted ensemble techniques. These approaches combine classification outcomes from multiple classifiers using soft voting (SVT) or weighted predictions (NWM). The proposed methods incorporate three distinct models: CNN, VGG-16, and InceptionResNetV2 trained on publicly available datasets. The performance of these methods is evaluated through blind testing, achieving exceptional results. The paper compares the performance of the proposed methods with SVT to demonstrate their superiority. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about finding better ways to diagnose brain tumors. Brain tumors are very serious and can be hard to detect, especially in poor countries. If we can catch them early, we can help people get better. Right now, diagnosing brain tumors takes a long time and costs a lot of money, which makes it even harder. The researchers created two new ways to combine information from different tests to make diagnoses. These new methods use three types of computer models: CNN, VGG-16, and InceptionResNetV2. They tested these methods and found that they work very well. |
Keywords
* Artificial intelligence * Classification * Cnn
