Summary of Optimized Learning For X-ray Image Classification For Multi-class Disease Diagnoses with Accelerated Computing Strategies, by Sebastian A. Cruz Romero et al.
Optimized Learning for X-Ray Image Classification for Multi-Class Disease Diagnoses with Accelerated Computing Strategies
by Sebastian A. Cruz Romero, Ivanelyz Rivera de Jesus, Dariana J. Troche Quinones, Wilson Rivera Gallego
First submitted to arxiv on: 1 Jul 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 This study aims to enhance the precision and reliability of X-ray image analysis algorithms for multi-class disease diagnosis. The challenge lies in reducing false positives and negatives, which can lead to misdiagnosis and delayed treatment. To address this, researchers introduced modified ResNet models tailored for disease diagnosis, incorporating optimization strategies to accelerate training and inference tasks. They used PyTorch, CUDA, Mixed-Precision Training, and Learning Rate Scheduler to achieve performance improvements. The study finds that executing training on a GPU with CUDA acceleration can significantly reduce execution time compared to normal training, but optimization modalities do not show significant differences. This research contributes to optimizing computational approaches for larger models and explores parallel data processing using MPI4Py. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary X-ray images are used to diagnose diseases, but finding the right disease can be tricky. If we make a mistake, it can lead to bad outcomes. To solve this problem, scientists created new computer algorithms that work faster and better than before. They tested these algorithms on special computers with super-fast processing power. The results show that using these powerful computers makes a big difference in how fast the algorithm works. This is important for making quick decisions when treating patients. |
Keywords
» Artificial intelligence » Inference » Optimization » Precision » Resnet
