Lung nodule detection in CT images using deep convolutional neural networks

Golan, Rotem; Jacob, Christian; Denzinger, Jörg

doi:10.1109/ijcnn.2016.7727205

Cited by 97 publications

(38 citation statements)

References 26 publications

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“…In [12], R. Golan proposed a framework that train the weights of the CNN by a back propagation to detect lung nodules in the CT image sub-volumes. This system achieved sensitivity of 78.9% with 20 false positives, while 71.2% with 10 FPs per scan, on lung nodules that have been annotated by all four radiologists Convolutional neural networks have achieved better than Deep Belief Networks in current studies on benchmark computer vision datasets.…”

Section: Methodsmentioning

confidence: 99%

“…A visualization of our 3D Googlenet is included in Figure 16 and described in detail in Table 4. Refer to Szegedy et al for more information on the inception module [12].…”

Section: Malignancy Classifiersmentioning

confidence: 99%

“…We shrunk the network to prevent parameter overload for the relatively small Kaggle dataset. A visualization of our vanilla 3D CNN architecture is included in Figure 15 and described in detail in Table 3 We also designed a 3D Googlenet-based model is based on the 2D model designed in Szegedy et al for image classification [12]. A visualization of our 3D Googlenet is included in Figure 16 and described in detail in Table 4.…”

Section: (A)mentioning

confidence: 99%

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

Alakwaa¹,

Nassef²,

Badr³

2017

ijacsa

184

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%

“…A visualization of our 3D Googlenet is included in Figure 16 and described in detail in Table 4. Refer to Szegedy et al for more information on the inception module [12].…”

Section: Malignancy Classifiersmentioning

confidence: 99%

Section: (A)mentioning

confidence: 99%

See 1 more Smart Citation

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

Alakwaa¹,

Nassef²,

Badr³

2017

ijacsa

184

100

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“…We performed the computation using a Computer with Intel Core i5-7200U CPU, 2.50 GHz, Intel HD Graphics 4000, 16 GB RAM, 64-bit Windows 10 OS. We have achieved the detection accuracy of about 80% which is greater than that of [8] [9].…”

Section: Resultsmentioning

confidence: 99%

Lung Cancer Detection Using CT Image Based on 3D Convolutional Neural Network

Ahmed¹,

Parvin²,

Haque³

et al. 2020

JCC

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Early detection of lung nodule is of great importance for the successful diagnosis and treatment of lung cancer. Many researchers have tried with diverse methods, such as thresholding, computer-aided diagnosis system, pattern recognition technique, backpropagation algorithm, etc. Recently, convolutional neural network (CNN) finds promising applications in many areas. In this research, we investigated 3D CNN to detect early lung cancer using LUNA 16 dataset. At first, we preprocessed raw image using thresholding technique. Then we used Vanilla 3D CNN classifier to determine whether the image is cancerous or non-cancerous. The experimental results show that the proposed method can achieve a detection accuracy of about 80% and it is a satisfactory performance compared to the existing technique.

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“…And by using LBP the cancer cell is detected. A review paper of Rotem Golan et.al proposed a backpropagation algorithm to train the Convolutional Neural Network for differentiating the size and shape of the lung nodule and also to extract volumetric feature from the input lung image [7].A.…”

Section: Related Workmentioning

confidence: 99%

A Novel ASNO Segmentation Technique for Segmenting the JUXTA Vascular Region

Mathews¹,

Jeyakumar²

2018

IJET

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In this modern era the clinical laboratory has greater attention to produce an accurate result for every test particularly in the area of lung tumour. The lung tumour is very essential to detect as well as to follow the treatment of many diseases like benign, malignant etc. This paper is focusing on the segmentation part to find the juxta vascular region. For finding the juxta vascular region in lung three stages are used. First stage is the image acquisition here input lung image is read and then resized. Second stage is the image pre-processing here improved linear iterative clustering technique is used .Third stage is the segmentation here the adjustable surface normal overlap is used. While using the above stages the output for juxta vascular region in the segmentation part is segmented clearly. The juxta vascular region is not clearly found in the previous paper. The research gap for this paper is to find the juxta vascular region in the lung. This juxta vascular region is present in the right side of the lung. Using the Adjustable Surface Normal Overlap (ASNO) segmentation the juxta vascular region is segmented clearly.

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Lung nodule detection in CT images using deep convolutional neural networks

Cited by 97 publications

References 26 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)

Lung Cancer Detection Using CT Image Based on 3D Convolutional Neural Network

A Novel ASNO Segmentation Technique for Segmenting the JUXTA Vascular Region

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