Prediction Models for Malignant Pulmonary Nodules Based-on Texture Features of CT Image

Guo, Xiuhua; Sun, Tao; Wang, Huan; Zhang, Liang

doi:10.5772/14766

Cited by 19 publications

(10 citation statements)

References 19 publications

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“…This imaging modality is useful in predicting the likelihood of achieving positive histocytology at Flexible Bronchoscopy. The overall CT prediction is superior to any of the individual CT features taken alone (Bungay HK et al, 2000) and (Xiuhua G et al, 2011).…”

Section: Computed Tomographymentioning

confidence: 82%

Global Perspectives on Bronchoscopy

Haranath¹,

Razvi²

2012

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Section: Computed Tomographymentioning

confidence: 82%

Global Perspectives on Bronchoscopy

Haranath¹,

Razvi²

2012

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Section: M I T a T I O N S S U C H A S H I G H C O S T U T I L I mentioning

confidence: 95%

“…Although histology diagnosis is the most accurate detection method in the medical environment, it is an aggressive invasive procedure that involves risks, discomfort and trauma, which restrict it to be used in the clinical practice (Xiuhua G et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Lung Cancer - CT Vs Bronchoscopy

Saraiva¹,

Maximino²

2012

Global Perspectives on Bronchoscopy

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“…The final decision depends on the radiologists or the doctors [12]. A radiologist needs careful observation of the density of pulmonary nodules, because at an early stage, this density may resemble the densities of other lung parts [13]. A CAD system tries to make a boundary of a pulmonary nodule by detecting some distinguishing features in the nodule.…”

Section: Introductionmentioning

confidence: 99%

Light Deep Model for Pulmonary Nodule Detection from CT Scan Images for Mobile Devices

Masud

Muhammad

Hossain

et al. 2020

Wireless Communications and Mobile Computing

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The emergence of cognitive computing and big data analytics revolutionize the healthcare domain, more specifically in detecting cancer. Lung cancer is one of the major reasons for death worldwide. The pulmonary nodules in the lung can be cancerous after development. Early detection of the pulmonary nodules can lead to early treatment and a significant reduction of death. In this paper, we proposed an end-to-end convolutional neural network- (CNN-) based automatic pulmonary nodule detection and classification system. The proposed CNN architecture has only four convolutional layers and is, therefore, light in nature. Each convolutional layer consists of two consecutive convolutional blocks, a connector convolutional block, nonlinear activation functions after each block, and a pooling block. The experiments are carried out using the Lung Image Database Consortium (LIDC) database. From the LIDC database, 1279 sample images are selected of which 569 are noncancerous, 278 are benign, and the rest are malignant. The proposed system achieved 97.9% accuracy. Compared to other famous CNN architecture, the proposed architecture has much lesser flops and parameters and is thereby suitable for real-time medical image analysis.

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Prediction Models for Malignant Pulmonary Nodules Based-on Texture Features of CT Image

Cited by 19 publications

References 19 publications

Global Perspectives on Bronchoscopy

Global Perspectives on Bronchoscopy

Lung Cancer - CT Vs Bronchoscopy

Light Deep Model for Pulmonary Nodule Detection from CT Scan Images for Mobile Devices

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