オープンデータ関連のニュースです。
日本語
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English
After training two deep-learning models to identify tuberculosis, researchers at Thomas Jefferson University in Philadelphia have gotten their human-free method to nail the disease with 96 percent accuracy, according to a study published online in Radiology. Paras Lakhani, MD, and Baskaran Sundaram, MD, split 1,007 x-rays of patients with and without active TB into training, validation and test datasets. They used the cases to train the AlexNet and GoogLeNet deep convolutional neural networks (DCNNs) to distinguish between TB-positive and TB-negative x-rays. The authors then tested the networks’ accuracy on 150 cases that were excluded from the training and validation datasets. It was a combination of the two DCNNs that achieved 96 percent accuracy.
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