Summary of Development Of Cnn Architectures Using Transfer Learning Methods For Medical Image Classification, by Ganga Prasad Basyal et al.
Development of CNN Architectures using Transfer Learning Methods for Medical Image Classification
by Ganga Prasad Basyal, David Zeng, Bhaskar Pm Rimal
First submitted to arxiv on: 22 Oct 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 research paper explores the development of Convolutional Neural Network (CNN) architectures utilizing transfer learning techniques in medical image classification. Specifically, it examines the application of deep learning-based models to tackle key challenges in this field using a timeline mapping model. The study aims to provide insights for selecting the most effective and state-of-the-art CNN architectures for medical image classification tasks. By leveraging transfer learning, the proposed approach demonstrates improved efficiency and accuracy in medical image classification. This research has implications for various medical imaging applications, including diagnosis and treatment planning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how to improve deep learning models used in medicine to classify images correctly. The researchers want to know which types of computer models work best for this task. They use a special way to organize the information to help make decisions about which models are the most effective. This can help doctors and hospitals get better results from medical image analysis, which is important for diagnosis and treatment. |
Keywords
» Artificial intelligence » Cnn » Deep learning » Image classification » Neural network » Transfer learning
