Lung Nodule Classification Based on Deep Convolutional Neural Networks
| Autor: | Helio Pedrini, Julio Cesar Mendoza Bobadilla |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Nodule detection
Contextual image classification business.industry Computer science Nodule (medicine) Pattern recognition CAD 02 engineering and technology medicine.disease Convolutional neural network 030218 nuclear medicine & medical imaging Radiological technology 03 medical and health sciences 0302 clinical medicine Stochastic gradient descent 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Artificial intelligence medicine.symptom Lung cancer business |
| Zdroj: | Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications ISBN: 9783319522760 CIARP |
| DOI: | 10.1007/978-3-319-52277-7_15 |
| Popis: | Lung nodule classification is one of the main topics on computer-aided diagnosis (CAD) systems for detecting nodules. Although convolutional neural networks (CNN) have been demonstrated to perform well on many tasks, there are few explorations of their use for classifying lung nodules. In this work, we present a method for classifying lung nodules based on CNNs. Training is performed by balancing the mini-batches on each stochastic gradient descent (SGD) iteration to address the lack of nodule samples compared to background samples. We show that our method outperforms a base feature-engineering method using the same techniques for other stages of lung nodule detection, and show that CNNs obtain competitive results when compared to state-of-the-art methods evaluated on Japanese Society of Radiological Technology (JSRT) dataset [13]. |
| Databáze: | OpenAIRE |
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