Deep-learning framework to detect lung abnormality - A study with chest X-Ray and lung CT scan images
Deep-learning framework to detect lung abnormality - A study with chest X-Ray and lung CT scan images
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DOI：
10.1016/j.patrec.2019.11.013
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发表时间：
2020-01-01
影响因子：
5.1
通讯作者：
Raja, N. Sri Madhava
中科院分区：
文献类型：
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作者：
Bhandary, Abhir;Prabhu, G. Ananth;Raja, N. Sri Madhava
肺部异常是人类非常危险的疾病。肺部异常的早期诊断对于通过快速有效的治疗来降低风险至关重要。这项研究工作旨在提出一个深度学习（DL）框架来检查肺部肺炎和癌症。这项工作提出了两种不同的深度学习技术来评估所考虑的问题：(i)最初的深度学习方法，命名为改进的AlexNet (MAN)，提出将胸部x射线图像分为正常和肺炎类。在MAN中，使用支持向量机（SVM）实现分类，并将其性能与Softmax进行比较。此外，它的性能与其他预训练的深度学习技术，如AlexNet， VGG16， VGG19和ResNet50进行了验证。（ii）第二个深度学习工作在MAN中实现了手工制作和学习特征的融合，以提高肺癌评估期间的分类准确性。该工作采用序列融合和基于主成分分析（PCA）的特征选择来增强特征向量。使用LIDC-IDRI的肺癌CT基准图像对DL框架的性能进行了测试，分类准确率达到97.27%。(C) 2019 Elsevier B.V.版权所有
Lung abnormalities are highly risky conditions in humans. The early diagnosis of lung abnormalities is essential to reduce the risk by enabling quick and efficient treatment. This research work aims to propose a Deep-Learning (DL) framework to examine lung pneumonia and cancer. This work proposes two different DL techniques to assess the considered problem: (i) The initial DL method, named a modified AlexNet (MAN), is proposed to classify chest X-Ray images into normal and pneumonia class. In the MAN, the classification is implemented using with Support Vector Machine (SVM), and its performance is compared against Softmax. Further, its performance is validated with other pre-trained DL techniques, such as AlexNet, VGG16, VGG19 and ResNet50. (ii) The second DL work implements a fusion of handcrafted and learned features in the MAN to improve classification accuracy during lung cancer assessment. This work employs serial fusion and Principal Component Analysis (PCA) based features selection to enhance the feature vector. The performance of this DL frame work is tested using benchmark lung cancer CT images of LIDC-IDRI and classification accuracy (97.27%) is attained. (C) 2019 Elsevier B.V. All rights reserved.