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Detection and classification of lung nodules in chest X‐ray images using deep convolutional neural networks
Author(s) -
Mendoza Julio,
Pedrini Helio
Publication year - 2020
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/coin.12241
Subject(s) - convolutional neural network , computer science , artificial intelligence , pattern recognition (psychology) , false positive paradox , nodule (geology) , deep learning , receiver operating characteristic , contextual image classification , image (mathematics) , machine learning , paleontology , biology
Lung nodule classification is one of the main topics related to computer‐aided detection systems. Although convolutional neural networks (CNNs) have been demonstrated to perform well on many tasks, there are few explorations of their use for classifying lung nodules in chest X‐ray (CXR) images. In this work, we proposed and analyzed a pipeline for detecting lung nodules in CXR images that includes lung area segmentation, potential nodule localization, and nodule candidate classification. We presented a method for classifying nodule candidates with a CNN trained from the scratch. The effectiveness of our method relies on the selection of data augmentation parameters, the design of a specialized CNN architecture, the use of dropout regularization on the network, inclusive in convolutional layers, and addressing the lack of nodule samples compared to background samples balancing mini‐batches on each stochastic gradient descent iteration. All model selection decisions were taken using a CXR subset of the Lung Image Database Consortium and Image Database Resource Initiative dataset separately. Thus, we used all images with nodules in the Japanese Society of Radiological Technology dataset for evaluation. Our experiments showed that CNNs were capable of achieving competitive results when compared to state‐of‐the‐art methods. Our proposal obtained an area under the free‐response receiver operating characteristic curve of 7.76 considering 10 false positives per image (FPPI), and sensitivity values of 73.1% and 79.6% with 2 and 5 FPPI, respectively.