Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images

Abstract

The present study shows that the performance of CNN is not significantly different from the best classical methods and human doctors for classifying mediastinal lymph node metastasis of NSCLC from PET/CT images. Because CNN does not need tumor segmentation or feature calculation, it is more convenient and more objective than the classical methods. However, CNN does not make use of the import diagnostic features, which have been proved more discriminative than the texture features for classifying small-sized lymph nodes. Therefore, incorporating the diagnostic features into CNN is a promising direction for future research.

References in corpus (3)

• Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
• Improving Computer-aided Detection using Convolutional Neural Networks and Random View Aggregation
• A New 2.5D Representation for Lymph Node Detection using Random Sets of Deep Convolutional Neural Network Observations

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