Comparing two classes of end-to-end machine-learning models in lung nodule detection and classification: MTANNs vs. CNNs

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• 作者：Nima Tajbakhsh ; ^{ntajbakh@iit.edu}Author Vitae ; Kenji Suzuki ^{ksuzuki@iit.edu}Author Vitae
• 关键词：Deep learning ; Patch-based machine learning ; Image-based machine learning ; Massive-training artificial neural network ; Convolution neural network ; Focal lesions ; Classification ; Computer-aided diagnosis ; Lung nodules
• 刊名：Pattern Recognition
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：63
• 期：Complete
• 页码：476-486
• 全文大小：2267 K
• 卷排序：63

文摘

MTANNs yielded higher performance than CNNs for nodule detection and classification. Deep CNN architectures achieved higher performance than shallow architectures for nodule detection. CNN architectures with varying depths performed comparably for nodule classification. MTANNs can achieve desired performance with a smaller training dataset than do the CNNs. MTANNs tend to learn the appearance of lesion parts, whereas CNNs attempt to learn the lesion appearance as a whole.