A comparative review of CNN architectures for detecting ten cancer types, summarizing advantages, disadvantages, and top performers for potential clinical use.
Then, the proposed model achieved 95.98% on the test data, which outperformed two pre-trained models, including GoogLeNet and MobileNet
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The Potential of Convolutional Neural Networks for Cancer Detection
A comparative review of CNN architectures for detecting ten cancer types, summarizing advantages, disadvantages, and top performers for potential clinical use.