Lung nodule diagnosis from CT images based on ensemble learning

Farahani, Farzad V.; Ahmadi, Abbas; Zarandi, M. H. Fazel

doi:10.1109/cibcb.2015.7300281

Cited by 40 publications

(17 citation statements)

References 27 publications

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“…The experiment results show that the proposed method outperforms most 2D network methods in the classification of lung nodules, including those multiple-2D-ConvNets-combination methods (e.g., the methods in Ref. [ 34 ] and [ 33 ]) and single-2D-ConvNet methods (e.g., the LUNA16CAD and LungNess methods 18 shown in Table IV), which is probably because the 3D ConvNet is more capable of processing 3D spatial information of the target than that of 2D ConvNet.…”

Section: E Methods Comparison With the State-of-the-artmentioning

confidence: 97%

Automatic classification of lung nodule candidates based on a novel 3D convolution network and knowledge transferred from a 2D network

Wang

Zhou²,

He³

et al. 2019

Medical Physics

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Objective In the automatic lung nodule detection system, the authenticity of a large number of nodule candidates needs to be judged, which is a classification task. However, the variable shapes and sizes of the lung nodules have posed a great challenge to the classification of candidates. To solve this problem, we propose a method for classifying nodule candidates through three‐dimensional (3D) convolution neural network (ConvNet) model which is trained by transferring knowledge from a multiresolution two‐dimensional (2D) ConvNet model. Methods In this scheme, a novel 3D ConvNet model is preweighted with the weights of the trained 2D ConvNet model, and then the 3D ConvNet model is trained with 3D image volumes. In this way, the knowledge transfer method can make 3D network easier to converge and make full use of the spatial information of nodules with different sizes and shapes to improve the classification accuracy. Results The experimental results on 551 065 pulmonary nodule candidates in the LUNA16 dataset show that our method gains a competitive average score in the false‐positive reduction track in lung nodule detection, with the sensitivities of 0.619 and 0.642 at 0.125 and 0.25 FPs per scan, respectively. Conclusions The proposed method can maintain satisfactory classification accuracy even when the false‐positive rate is extremely small in the face of nodules of different sizes and shapes. Moreover, as a transfer learning idea, the method to transfer knowledge from 2D ConvNet to 3D ConvNet is the first attempt to carry out full migration of parameters of various layers including convolution layers, full connection layers, and classifier between different dimensional models, which is more conducive to utilizing the existing 2D ConvNet resources and generalizing transfer learning schemes.

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Section: E Methods Comparison With the State-of-the-artmentioning

confidence: 97%

Automatic classification of lung nodule candidates based on a novel 3D convolution network and knowledge transferred from a 2D network

Wang

Zhou²,

He³

et al. 2019

Medical Physics

View full text Add to dashboard Cite

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“…According to Report by [2], [3], lung cancer becomes one of the deadliest diseases that cause 1.2-1.56 million deaths annually. It is predicted that those number will continue to increase and will reach 17 million lung cancer case by 2030 [4]. From the whole lung cancer cases, 80% of the cases are identified after medium stage [4], which means that it will more difficult to handle by medical treatment.…”

Section: Introductionmentioning

confidence: 99%

“…It is predicted that those number will continue to increase and will reach 17 million lung cancer case by 2030 [4]. From the whole lung cancer cases, 80% of the cases are identified after medium stage [4], which means that it will more difficult to handle by medical treatment. Hence, early detection by identifying the appearance of lung nodule will be able to provide better handling to the patient.…”

Section: Introductionmentioning

confidence: 99%

CNN and SVM Based Classifier Comparation to Detect Lung Nodule In Computed Tomography Images

Sentana¹,

Asri²,

Jawas³

et al. 2018

Proceedings of the International Conference on Science and Technology (ICST 2018)

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Convolutional Neural Networks (CNN) are natural based classification algorithm that combine Multiple Layer Perceptron (MLPs). Meanwhile, support vector machines (SVM) is a mathematical-based classification algorithm that naturally have supervised learning models. In some research related to image processing, each algorithm has its owned supremacy as well as the drawback. None of the previous studies compare both algorithm when they are utilized to detect nodule located in the pulmonary or lung images produced by Computed Tomography (CT) scan. Hence, this research comparing the two algorithms in case of lung nodule detection in CT images, since detecting lung nodule in CT images is still challenging. SVM-based classifier is preceded by feature extraction as its common behavior of mathematical based classifier. There are three algorithms use to conduct feature extraction process, namely Hu moment invariant, Haralick and Color Histogram extraction. In the opposite, CNN-based classifier consists of three layers convolution for training and testing steps. The result shows that SVM has better results than CNN in case of computing speed. Meanwhile have a better accuracy in detecting lung nodule. The results of the test analysis show that the extractor feature when preprocessing conduct before being classified by SVM makes the computing process faster. The accuracy of SVM-based classifier can be improved by adjusting some computation variables in feature extraction stages, such as adding more bins in the color histogram extraction. Those adjustment will lead to more computation times.

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“…The number will continue to increase, which in 2030 is predict to occur 17 million deaths from lung cancer [4]. From the research conducted by [5], 80% of new lung cancers are known after suffering from middle to upper stage disease. The existence of early detection based on lung nodule will be able to provide better handling to the patient.…”

Section: Introductionmentioning

confidence: 99%

Automatic Solitary Lung Nodule Detection in Computed Tomography Images Slices

Sentana

Jawas

Asri

2018

J. Phys.: Conf. Ser.

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Abstract. Lung nodule is an early indicator of some lung diseases, including lung cancer. In Computed Tomography (CT) based image, nodule is known as a shape that appears brighter than lung surrounding. This research aim to develop an application that automatically detect lung nodule in CT images. There are some steps in algorithm such as image acquisition and conversion, image binarization, lung segmentation, blob detection, and classification. Data acquisition is a step to taking image slice by slice from the original *.dicom format and then each image slices is converted into *.tif image format. Binarization that tailoring Otsu algorithm, than separated the background and foreground part of each image slices. After removing the background part, the next step is to segment part of the lung only so the nodule can localized easier. Once again Otsu algorithm is use to detect nodule blob in localized lung area. The final step is tailoring Support Vector Machine (SVM) to classify the nodule. The application has succeed detecting near round nodule with a certain threshold of size. Those detecting result shows drawback in part of thresholding size and shape of nodule that need to enhance in the next part of the research. The algorithm also cannot detect nodule that attached to wall and Lung Chanel, since it depend the searching only on colour differences.

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Lung nodule diagnosis from CT images based on ensemble learning

Cited by 40 publications

References 27 publications

Automatic classification of lung nodule candidates based on a novel 3D convolution network and knowledge transferred from a 2D network

Automatic classification of lung nodule candidates based on a novel 3D convolution network and knowledge transferred from a 2D network

CNN and SVM Based Classifier Comparation to Detect Lung Nodule In Computed Tomography Images

Automatic Solitary Lung Nodule Detection in Computed Tomography Images Slices

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