Classification of margin characteristics from 3D pulmonary nodules

Paing, May Phu; Choomchuay, Somsak

doi:10.1109/bmeicon.2017.8229104

Cited by 5 publications

(5 citation statements)

References 16 publications

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“…In the end, two lung regions were extracted from the labeled image, the largest and next largest connected regions, and the rest of the segmented regions set as the background [23], [24]. The threshold segmentation of the lungs is the work of [18], [19], [21]- [23], [25]- [27], [28]- [36]. Otsu threshold [28], [37], segmentation on the basis of the iterative threshold [9], [38], [39], adaptive threshold [26], [40], The literature has an adaptive 3-D fuzzy threshold [41].…”

Section: B Lung Segmentationmentioning

confidence: 99%

“…The threshold segmentation of the lungs is the work of [18], [19], [21]- [23], [25]- [27], [28]- [36]. Otsu threshold [28], [37], segmentation on the basis of the iterative threshold [9], [38], [39], adaptive threshold [26], [40], The literature has an adaptive 3-D fuzzy threshold [41]. Pulmonary segmentation on the basis of region and 3D region can be located in [5], [42]- [48].…”

Section: B Lung Segmentationmentioning

confidence: 99%

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Computer-aided diagnostic system kinds and pulmonary nodule detection efficacy

Kadhim

Motlak²,

Abdalla³

2022

IJECE

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This paper summarizes the literature on computer-aided detection (CAD) systems used to identify and diagnose lung nodules in images obtained with computed tomography (CT) scanners. The importance of developing such systems lies in the fact that the process of manually detecting lung nodules is painstaking and sequential work for radiologists, as it takes a long time. Moreover, the pulmonary nodules have multiple appearances and shapes, and the large number of slices generated by the scanner creates great difficulty in accurately locating the lung nodules. The handcraft nodules detection process can be caused by messing some nodules spicily when these nodules' diameter be less than 10 mm. So, the CAD system is an essential assistant to the radiologist in this case of nodule detection, and it contributed to reducing time consumption in nodules detection; moreover, it applied more accuracy in this field. The objective of this paper is to follow up on current and previous work on lung cancer detection and lung nodule diagnosis. This literature dealt with a group of specialized systems in this field quickly and showed the methods used in them. It dealt with an emphasis on a system based on deep learning involving neural convolution networks.

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Section: B Lung Segmentationmentioning

confidence: 99%

Section: B Lung Segmentationmentioning

confidence: 99%

Computer-aided diagnostic system kinds and pulmonary nodule detection efficacy

Kadhim

Motlak²,

Abdalla³

2022

IJECE

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“…In recent years, 3D-based features were commonly used to represent the lung nodule image. They were adopted in References [18,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,78,79,80,81,82,83,88,89,90,96,97,98,105,110,123,124,125,126,127,128,129,130].…”

Section: Analysis Of Selected Workmentioning

confidence: 99%

“…In Reference [130], thresholding and morphological operations were used to extract nodule candidates in 2D images, and then these candidates were stacked to structure 3D image. The region of the 3D image was manually cropped.…”

Section: Analysis Of Selected Workmentioning

confidence: 99%

“…The main methods for classifier construction are machine learning-based, which uses the training algorithm and labeled data. Many kinds of classifiers are used in selected papers, such as those based on support vector machines (SVM) [29,34,35,36,37,39,41,46,47,49,62,63,64,65,70,71,75,80,81,92,94,96,99,100,101,102,107,110,115,130], bayesian classifier [35,40,46,62,98,117], rule based [106], k-means [57,58,86], linear discriminant analysis (LDA) [33,46,54,57,58,68,72,76,82,88], artificial neural networks (ANNs) [44,45,50,55,62,66,69,77,78,79,104,112,121,122], convolutional Neural Networks (CNNs) […”

Section: Main Process Introductionmentioning

confidence: 99%

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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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Multi‐level deep learning based lung cancer classifier for classification based on tumour‐node‐metastasis approach

Pawar¹,

Talbar

2022

Int J Imaging Syst Tech

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Treatment of non‐small cell lung cancer depends on detecting the cancer stage. The oncologist decides the cancer stage based on the tumour‐node‐metastasis (TNM) staging suggested by the American Joint Committee on Cancer (AJCC). This study simplifies the complicated problem of classifying computed tomography (CT) images into TNM‐based classes using deep learning algorithms at various levels. In the first level, an optimised conditional generative adversarial network (c‐GAN) network is developed for automatic lung segmentation, including nodules within the lung and juxtapleural nodules. Earlier studies used time‐consuming manual identification of the region of interest patches from the lung CT image before applying the deep learning classification algorithm. At the next level, three different deep learning algorithms, along with three support vector machine classifiers, are used for the classification of Tumour, Node and Metastasis as per the AJCC staging nomenclature. The specially designed c‐GAN network's performance is maximised using the Taguchi approach, which helps automatically preprocess CT images by removing unwanted background noises. Further, three different pre‐trained Resnet50 networks are trained using transfer learning for extracting the deep features for finally applying to three different classifiers, resulting in three different classes. The comparative segmentation performance assessment in the form of the average dice similarity coefficient and Jaccard index indicates that the proposed c‐GAN gives the best segmentation performance of the lung without losing the nodule compared to other segmentation algorithms. The proposed approach gives the classification performance for the Tumour as 91.94%–97.32%, the Nodule as 91.99%–100%, and the Metastasis as 99.25%–100.00%.

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Classification of margin characteristics from 3D pulmonary nodules

Cited by 5 publications

References 16 publications

Computer-aided diagnostic system kinds and pulmonary nodule detection efficacy

Computer-aided diagnostic system kinds and pulmonary nodule detection efficacy

An Appraisal of Lung Nodules Automatic Classification Algorithms for CT Images

Multi‐level deep learning based lung cancer classifier for classification based on tumour‐node‐metastasis approach

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