Lung Nodule Classification Using Deep Features in CT Images

Kumar, Divya; Wong, Alexander; Clausi, David A.

doi:10.1109/crv.2015.25

Cited by 318 publications

(209 citation statements)

References 19 publications

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“…Xu et al [7] presented the effectiveness of using deep neural networks (DNNs) for feature extraction in medical image analysis as a supervised approach. Kumar et al [8] proposed a CAD system which uses deep features extracted from an autoencoder to classify lung nodules as either malignant or benign on LIDC database. In [9], Yaniv et al presented a system for medical application of chest pathology detection in x-rays which uses convolutional neural networks that are learned from a non-medical archive.…”

Section: Methodsmentioning

confidence: 99%

“…Pixels thresholded at 400 HU are shown in Figure 3, and the mask is shown in Figure 4 However, to account for the possibility that some cancerous growth could occur within the bronchioles (air pathways) inside the lung, which are shown in Figure 5, we choose to include this air to create the finalized mask as shown in Figure 6. Substance Radiodensity (HU) Air -1000 Lung tissue -500 water and blood 0 bone 700 Table 1: typical radiodensities in HU of various substances in a CT scan [8] …”

Section: Thresholdingmentioning

confidence: 99%

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Lung Cancer Detection and Classification with 3D Convolutional Neural Network (3D-CNN)

Alakwaa¹,

Nassef²,

Badr³

2017

ijacsa

197

100

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Abstract-This paper demonstrates a computer-aided diagnosis (CAD) system for lung cancer classification of CT scans with unmarked nodules, a dataset from the Kaggle Data Science Bowl, 2017. Thresholding was used as an initial segmentation approach to segment out lung tissue from the rest of the CT scan. Thresholding produced the next best lung segmentation. The initial approach was to directly feed the segmented CT scans into 3D CNNs for classification, but this proved to be inadequate. Instead, a modified U-Net trained on LUNA16 data (CT scans with labeled nodules) was used to first detect nodule candidates in the Kaggle CT scans. The U-Net nodule detection produced many false positives, so regions of CTs with segmented lungs where the most likely nodule candidates were located as determined by the U-Net output were fed into 3D Convolutional Neural Networks (CNNs) to ultimately classify the CT scan as positive or negative for lung cancer. The 3D CNNs produced a test set Accuracy of 86.6%. The performance of our CAD system outperforms the current CAD systems in literature which have several training and testing phases that each requires a lot of labeled data, while our CAD system has only three major phases (segmentation, nodule candidate detection, and malignancy classification), allowing more efficient training and detection and more generalizability to other cancers.

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Section: Methodsmentioning

confidence: 99%

Section: Thresholdingmentioning

confidence: 99%

Lung Cancer Detection and Classification with 3D Convolutional Neural Network (3D-CNN)

Alakwaa¹,

Nassef²,

Badr³

2017

ijacsa

197

100

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show abstract

“…In contrast to the previous studies that used pre-trained network [8,9], in this work, we proposed an end-to-end training of deep multi-view Convolutional Neural Network for nodule malignancy determination termed TumorNet. In order to cater to the need to have a large amount of labeled data for CNN, we performed data augmentation using scale, rotation and different categories of noise.…”

Section: Discussionmentioning

confidence: 99%

“…This has also attracted the attention of researchers working in lung nodule detection and classification with limited success since the feature learning and classification were considered as separate modules. In those frameworks a pre-trained CNN was only used for feature extraction whereas classification was based on an off-the-shelf classifier such as RF [8,9]. In sharp contrast to these methods, we perform an end-to-end training of CNN for nodule characterization while combining multi-view features to obtain improved characterization performance.…”

Section: Introductionmentioning

confidence: 99%

TumorNet: Lung nodule characterization using multi-view Convolutional Neural Network with Gaussian Process

Hussein

Gillies

Cao

et al. 2017

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)

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Characterization of lung nodules as benign or malignant is one of the most important tasks in lung cancer diagnosis, staging and treatment planning. While the variation in the appearance of the nodules remains large, there is a need for a fast and robust computer aided system. In this work, we propose an end-to-end trainable multi-view deep Convolutional Neural Network (CNN) for nodule characterization. First, we use median intensity projection to obtain a 2D patch corresponding to each dimension. The three images are then concatenated to form a tensor, where the images serve as different channels of the input image. In order to increase the number of training samples, we perform data augmentation by scaling, rotating and adding noise to the input image. The trained network is used to extract features from the input image followed by a Gaussian Process (GP) regression to obtain the malignancy score. We also empirically establish the significance of different high level nodule attributes such as calcification, sphericity and others for malignancy determination. These attributes are found to be complementary to the deep multi-view CNN features and a significant improvement over other methods is obtained.

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“…Deep features obtained from sample learning show excellent ability to describe tumor characteristics [1], and the deep features are highly correlated with traditional features, indicating that deep features have the capabilities of mining image information [2]. Kumar D et al [3] proposed a CAD system that uses deep features extracted from an autoencoder to classify lung nodules. Wang H et al [4] presented a cascaded approach for mitosis detection that intelligently combines a CNN model and handcrafted features for breast cancer.…”

Section: Introductionmentioning

confidence: 99%

Staging of Hepatocellular Carcinoma Using Deep Feature in Contrast-Enhanced MR Images

Wang¹,

Que²

2017

Proceedings of the 2nd International Conference on Computer Engineering, Information Science &Amp; Application Technology (ICCI

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Abstract. Clinical stage of hepatocellular carcinoma (HCC) is of great significance for prognosis. Texture features of HCC in Contrast-enhanced MR images have been effective for predictions of staging. However, texture features are low-level features, which are usually insufficient to capture the complicated characteristics of HCCs. Recently, some studies have been dedicated to learning features in a data driven way for predictions. In this study, we use deep learning that can extract highlevel features in order to more accurately staging HCCs. Experimental results demonstrate that deep feature outperforms traditional texture features for HCC staging, and ensembles of deep features derived from multiview observations of HCCs yield best results.

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Lung Nodule Classification Using Deep Features in CT Images

Cited by 318 publications

References 19 publications

Lung Cancer Detection and Classification with 3D Convolutional Neural Network (3D-CNN)

Lung Cancer Detection and Classification with 3D Convolutional Neural Network (3D-CNN)

TumorNet: Lung nodule characterization using multi-view Convolutional Neural Network with Gaussian Process

Staging of Hepatocellular Carcinoma Using Deep Feature in Contrast-Enhanced MR Images

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