Multistage segmentation model and SVM-ensemble for precise lung nodule detection

Naqi, Syed Muhammad; Sharif, Muhammad; Yasmin, Mussarat

doi:10.1007/s11548-018-1715-9

Cited by 59 publications

(29 citation statements)

References 31 publications

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“…The segmentation is based upon directly on pixel intensities. Due to the overlap between the background intensities and some sections of ROI, simple thresholding may not be suitable for extraction of lung region 24 . The background region of the respective images is discarded to overcome this problem.…”

Section: Methodsmentioning

confidence: 99%

Effective and Reliable Framework for Lung Nodules Detection from CT Scan Images

Khan

Hussain

Yang

et al. 2019

Sci Rep

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Lung cancer is considered more serious among other prevailing cancer types. One of the reasons for it is that it is usually not diagnosed until it has spread and by that time it becomes very difficult to treat. Early detection of lung cancer can significantly increase the chances of survival of a cancer patient. An effective nodule detection system can play a key role in early detection of lung cancer thus increasing the chances of successful treatment. In this research work, we have proposed a novel classification framework for nodule classification. The framework consists of multiple phases that include image contrast enhancement, segmentation, optimal feature extraction, followed by employment of these features for training and testing of Support Vector Machine. We have empirically tested the efficacy of our technique by utilizing the well-known Lung Image Consortium Database (LIDC) dataset. The empirical results suggest that the technique is highly effective for reducing the false positive rates. We were able to receive an impressive sensitivity rate of 97.45%.

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Section: Methodsmentioning

confidence: 99%

Effective and Reliable Framework for Lung Nodules Detection from CT Scan Images

Khan

Hussain

Yang

et al. 2019

Sci Rep

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“…[5][6][7] Computer-aided detection systems typically consisted of two stages: (a) nodule candidate generation and (b) false positive reduction. Traditionally, in the first stage, some algorithms, including selective enhancement filter, [8][9][10][11] threshold, 10,12,13 and morphological operation, [13][14][15] were often employed to generate a large number of nodule candidates. In the second stage, a series of features, such as intensity, shape, and texture, were often extracted and selected to represent the characteristics of nodule candidates.…”

Section: Introductionmentioning

confidence: 99%

“…In the second stage, a series of features, such as intensity, shape, and texture, were often extracted and selected to represent the characteristics of nodule candidates. Then, classifiers such as support vector machine (SVM), 10,11,13,15,16 random forest, 12,17,18 and rule-based classifier 8,16 could be used to distinguish true nodules from false positives (FPs).…”

Section: Introductionmentioning

confidence: 99%

The effect of pulmonary vessel suppression on computerized detection of nodules in chest CT scans

Xie

Fang

et al. 2020

Medical Physics

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In chest computed tomography (CT) scans, pulmonary vessel suppression can make pulmonary nodules more evident, and therefore may increase the detectability of early lung cancer. The purpose of this study was to develop a computer-aided detection (CAD) system with a vessel suppression function and to verify the effectiveness of the vessel suppression on the performance of the pulmonary nodule CAD system. Methods: A CAD system with a vessel suppression function capable of suppressing vessels and detecting nodules was developed. First, a convolutional neural network (CNN)-based pulmonary vessel suppression technique was employed to remove the vessels from lungs while preserving the nodules. Then, a CNN-based pulmonary nodule detector was utilized to sequentially generate nodule candidates and reduce false positives (FPs). The performance levels of CAD systems with and without the vessel suppression function were compared using 888 three-dimensional chest CT scans from the Lung Nodule Analysis 2016 (LUNA16) dataset. The pulmonary nodule detection results were quantitatively evaluated using the average sensitivity at seven predefined FP rates: 0.125, 0.25, 0.5, 1, 2, 4, and 8 FPs per scan. Results: The developed pulmonary nodule CAD system improved the average sensitivity to 0.977 from 0.950 owing to the addition of the vessel suppression function. Conclusions: The vessel suppression function considerably improved the performance of the CAD system for pulmonary nodule detection. In practice, it would be embedded in CAD systems to assist radiologists in detecting pulmonary nodules in chest CT scans.

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“…Accordingly, this article presents an automated approach that provides quantitative and objective measurements of lung lesion detection and classification results with high reliability and reproducibility. The lung lesion in the CT image is shown in Figure (Naqi, Sharif, & Yasmin, ). The further article is organized into four main sections, Section 2 presents the research background, Section 3 presents the proposed methodology, Section 4 exhibits results, discussion, and finally Section 5 concludes the research.…”

Section: Introductionmentioning

confidence: 99%

Automated lung nodule detection and classification based on multiple classifiers voting

Saba

2019

Microscopy Res & Technique

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Lung cancer is the most common cause of cancer‐related death globally. Currently, lung nodule detection and classification are performed by radiologist‐assisted computer‐aided diagnosis systems. However, emerged artificially intelligent techniques such as neural network, support vector machine, and HMM have improved the detection and classification process of cancer in any part of the human body. Such automated methods and their possible combinations could be used to assist radiologists at early detection of lung nodules that could reduce treatment cost, death rate. Literature reveals that classification based on voting of classifiers exhibited better performance in the detection and classification process. Accordingly, this article presents an automated approach for lung nodule detection and classification that consists of multiple steps including lesion enhancement, segmentation, and features extraction from each candidate's lesion. Moreover, multiple classifiers logistic regression, multilayer perceptron, and voted perceptron are tested for the lung nodule classification using k‐fold cross‐validation process. The proposed approach is evaluated on the publically available Lung Image Database Consortium benchmark data set. Based on the performance evaluation, it is observed that the proposed method performed better in the stateof the art and achieved an overall accuracy rate of 100%.

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Multistage segmentation model and SVM-ensemble for precise lung nodule detection

Abstract: A lung nodule detection method is presented to facilitate radiologists in accurately diagnosing cancer from CT images. Results indicate that the proposed method has not only reduced FPs/scan but also significantly improved sensitivity as compared to related studies.

Cited by 59 publications

References 31 publications

Effective and Reliable Framework for Lung Nodules Detection from CT Scan Images

Effective and Reliable Framework for Lung Nodules Detection from CT Scan Images

The effect of pulmonary vessel suppression on computerized detection of nodules in chest CT scans

Automated lung nodule detection and classification based on multiple classifiers voting

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