Classification of Lung Nodules Based on Convolutional Deep Belief Network

Jin, Xinyu; Ma, Chunhui; Zhang, Yuchen; Li, Lanjuan

doi:10.1109/iscid.2017.57

Cited by 6 publications

(3 citation statements)

References 6 publications

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“…The parameters in the deep model can be automatically adjusted. Deep learning models, such as CNNs, Auto-encoder, DBNs et al were used in lung nodule image classification [18,42,43,44,45,60,77,83,92,95,96,103,105,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129].…”

Section: Analysis Of Selected Workmentioning

confidence: 99%

“…A Convolutional Deep Belief Networks (CDBNs) model was proposed in Reference [111]. Three convolutional restricted Boltzmann machine (CRBM) layers were trained by unsupervised methods.…”

Section: Analysis Of Selected Workmentioning

confidence: 99%

“…Selected papers based on deep feature are given in References [18,42,43,44,45,60,77,83,92,95,96,103,105,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129]. Selected papers that based on 3D features are given in References [18,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,78,79,80,81,82,83,88,89,90,96,97,98,105,110,123,124,125,…”

Section: Main Process Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Appraisal of Lung Nodules Automatic Classification Algorithms for CT Images

Wang

Mao

Wang

et al. 2019

Sensors

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Lung cancer is one of the most deadly diseases around the world representing about 26% of all cancers in 2017. The five-year cure rate is only 18% despite great progress in recent diagnosis and treatment. Before diagnosis, lung nodule classification is a key step, especially since automatic classification can help clinicians by providing a valuable opinion. Modern computer vision and machine learning technologies allow very fast and reliable CT image classification. This research area has become very hot for its high efficiency and labor saving. The paper aims to draw a systematic review of the state of the art of automatic classification of lung nodules. This research paper covers published works selected from the Web of Science, IEEEXplore, and DBLP databases up to June 2018. Each paper is critically reviewed based on objective, methodology, research dataset, and performance evaluation. Mainstream algorithms are conveyed and generic structures are summarized. Our work reveals that lung nodule classification based on deep learning becomes dominant for its excellent performance. It is concluded that the consistency of the research objective and integration of data deserves more attention. Moreover, collaborative works among developers, clinicians, and other parties should be strengthened.

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Section: Analysis Of Selected Workmentioning

confidence: 99%

“…A Convolutional Deep Belief Networks (CDBNs) model was proposed in Reference [111]. Three convolutional restricted Boltzmann machine (CRBM) layers were trained by unsupervised methods.…”

Section: Analysis Of Selected Workmentioning

confidence: 99%