Summary of The Potential Of Convolutional Neural Networks For Cancer Detection, by Hossein Molaeian et al.
The Potential of Convolutional Neural Networks for Cancer Detection
by Hossein Molaeian, Kaveh Karamjani, Sina Teimouri, Saeed Roshani, Sobhan Roshani
First submitted to arxiv on: 22 Dec 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 In this paper, the authors investigate the potential of Convolutional Neural Networks (CNNs) for early cancer detection. They analyze 10 different studies that employ CNN techniques for classifying medical images and identifying various types of cancer. By comparing these architectures, the authors highlight their strengths and limitations in terms of improving early detection rates. The study also explores the feasibility of integrating CNNs into clinical settings as a potential substitute or complement to traditional diagnostic methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers used Convolutional Neural Networks (CNNs) to analyze medical images for cancer detection. They looked at 10 different studies that tried to identify different types of cancer using these networks. By comparing the different ways they did this, the study found what worked well and what didn’t. The goal is to use these networks to improve early detection rates. |
Keywords
» Artificial intelligence » Cnn
