Deep CNN models for pulmonary nodule classification: Model modification, model integration, and transfer learning

Zhao, Xin; Qi, Shouliang; Zhang, Baihua; Ma, He; Qian, Wei; Yao, Yu-Dong; Sun, Jianjun

doi:10.3233/xst-180490

Cited by 46 publications

(31 citation statements)

References 26 publications

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“…In summary, we in this paper present a new deep transfer learning model to detect and classify the COVID-19 infected pneumonia cases, as well as several unique image preprocessing approaches to optimally train the deep learning model using the limited and unbalanced medical image dataset. The similar learning concept and image preprocessing approaches can also be adopted to develop new deep learning models for other medical images to detect and classify other types of diseases (i.e., cancers [ 30 , 31 ]).…”

Section: Discussionmentioning

confidence: 99%

Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms

Heidari

Mirniaharikandehei

Khuzani

et al. 2020

International Journal of Medical Informatics

343

211

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Highlights Radiographic chest images can be used to more accurately detect COVID-19 and assess disease severity. Among different imaging modalities, chest X-ray radiography has advantages of low cost, low radiation dose, wide accessibility and easy-to-operate in general or community hospitals. This study aims to develop and test a new deep learning model of chest X-ray images to detect COVID-19 induced pneumonia. For this purpose, we assembled a relatively large chest X-ray image dataset involving 8,474 cases, which are divided into three groups of COVID-19 infected pneumonia, other community-acquired no-COVID-19 infected pneumonia, and normal (non-pneumonia) cases. After applying a preprocessing algorithm to detect and remove diaphragm regions depicting on images, a histogram equalization algorithm and a bilateral filter are applied to process the original images to generate two sets of filtered images. Then, the original image plus these two filtered images are used as inputs of three channels of the CNN deep learning model, which increase learning information of the model. In order to fully take advantages of the pre-optimized CNN models, this study uses a transfer learning method to build a new model to detect and classify COVID-19 infected pneumonia. A VGG16 based CNN model was originally trained using ImageNet and fine-tuned using chest X-ray images in this study. To reduce the bias in training and testing the CNN model, dataset is randomly divided into 3 subsets namely, training, validation, and testing with respect to the same frequency of cases in each class in all three COVID-19 infected pneumonia, other community-acquired no-COVID-19 infected pneumonia, and normal (non-pneumonia) groups. Testing on a subset of 2544 cases, the CNN model yields 94.5% accuracy in classifying three subsets of cases and 98.1% accuracy in detecting COVID-19 infected pneumonia cases, which are significantly higher than the model directly trained using the original images without applying two image preprocessing steps to remove diaphragm and generate two filtered images.

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Section: Discussionmentioning

confidence: 99%

Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms

Heidari

Mirniaharikandehei

Khuzani

et al. 2020

International Journal of Medical Informatics

343

211

View full text Add to dashboard Cite

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“…There are several ways to obtain effective CNN models for medical imaging analysis: the specially designed CNNs, the typical off-the-shelf CNNs trained from scratch [53], [62]. We have tried these strategies in the current studies.…”

Section: E Specially Designed Cnns Vs Typical Off-the-shelf Cnnsmentioning

confidence: 99%

Identification of COPD From Multi-View Snapshots of 3D Lung Airway Tree via Deep CNN

Feng³

et al. 2020

IEEE Access

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Chronic obstructive pulmonary disease (COPD) is associated with morphologic abnormalities of airways with various patterns and severities. However, the way of effectively representing these abnormalities is lacking and whether these abnormalities enable to distinguish COPD from healthy controls is unknown. We propose to use deep convolutional neural network (CNN) to assess 3D lung airway tree from the perspective of computer vision, thereby constructing models of identifying COPD. After extracting airway trees from CT images, snapshots of their 3D visualizations are obtained from ventral, dorsal and isometric views. Using snapshots of each view, one deep CNN model is constructed and further optimized by Bayesian optimization algorithm to indentify COPD. The majority voting of three views presents the final prediction. Finally, the class-discriminative localization maps have been drawn to visually explain the CNNs' decisions. The models trained with single view (ventral, dorsal and isometric) of colorful snapshots present the similar accuracy (ACC) (86.8%, 87.5% and 86.7%) and the model after voting achieves the ACC of 88.2%. The ACC of the final voting model using gray and binary snapshots achieves 88.6% and 86.4%, respectively. Our specially designed CNNs outperform the typical off-the-shelf CNNs and the pre-trained CNNs with fine tuning. The class-discriminative regions of COPD are mainly located at central airways; however, regions in HC are scattering and located at peripheral airways. It is feasible to identify COPD using snapshots of 3D lung airway tree extracted from CT images via deep CNN. The CNNs can represent the abnormalities of airway tree in COPD and make accurate CT-based diagnosis of COPD. INDEX TERMS Chronic obstructive pulmonary disease (COPD), deep learning, convolutional neural networks, computed tomography (CT), airway, image classification.

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“…Even the sella-nasion-A point (SNA) angle and sella-nasion-B point (SNB) angle, which are the measurement values of the anterior and posterior positions of the maxilla and the mandible, may show different values even in the same skeletal state due to the change in the position of the nasion [10]. Therefore, in measurement-based evaluation, the supplementary decision of the clinician is important.Artificial intelligence (AI), especially deep learning using convolutional neural networks (CNNs), has become the most sought-after field [11][12][13][14]. With the development of computing hardware and graphics processing units, the computational processing speed has increased, complex calculations can be performed in a short time, and deep learning using deep convolutional neural networks (DCNNs) has been activated [15,16].…”

mentioning

confidence: 99%

“…Artificial intelligence (AI), especially deep learning using convolutional neural networks (CNNs), has become the most sought-after field [11][12][13][14]. With the development of computing hardware and graphics processing units, the computational processing speed has increased, complex calculations can be performed in a short time, and deep learning using deep convolutional neural networks (DCNNs) has been activated [15,16].…”

mentioning

confidence: 99%

Deep Convolutional Neural Networks Based Analysis of Cephalometric Radiographs for Differential Diagnosis of Orthognathic Surgery Indications

et al. 2020

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The aim of this study was to evaluate the deep convolutional neural networks (DCNNs) based on analysis of cephalometric radiographs for the differential diagnosis of the indications of orthognathic surgery. Among the DCNNs, Modified-Alexnet, MobileNet, and Resnet50 were used, and the accuracy of the models was evaluated by performing 4-fold cross validation. Additionally, gradient-weighted class activation mapping (Grad-CAM) was used to perform visualized interpretation to determine which region affected the DCNNs’ class classification. The prediction accuracy of the models was 96.4% for Modified-Alexnet, 95.4% for MobileNet, and 95.6% for Resnet50. According to the Grad-CAM analysis, the most influential regions for the DCNNs’ class classification were the maxillary and mandibular teeth, mandible, and mandibular symphysis. This study suggests that DCNNs-based analysis of cephalometric radiograph images can be successfully applied for differential diagnosis of the indications of orthognathic surgery.

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Deep CNN models for pulmonary nodule classification: Model modification, model integration, and transfer learning

Cited by 46 publications

References 26 publications

Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms

Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms

Identification of COPD From Multi-View Snapshots of 3D Lung Airway Tree via Deep CNN

Deep Convolutional Neural Networks Based Analysis of Cephalometric Radiographs for Differential Diagnosis of Orthognathic Surgery Indications

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