Health of Things Algorithms for Malignancy Level Classification of Lung Nodules

Rodrigues, Murillo Barata; Nóbrega, Raul Victor Medeiros da; Alves, Shara S. A.; Gupta, Deepak; Duarte, João Batista Furlan; Sangaiah, Arun Kumar; Albuquerque, Victor Hugo C. de

doi:10.1109/access.2018.2817614

Cited by 85 publications

(35 citation statements)

References 19 publications

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“…Comparative experiment. The present study further compared the performance of the proposed method based on the weighted voting classification method and similar classification methods (13,(33)(34)(35)(36) used under the same conditions (Table IV).…”

Section: Algorithm 1 Weighted Voting Algorithm Based On Classificatiomentioning

confidence: 99%

Ensemble classification for predicting the malignancy level of pulmonary nodules on chest computed tomography images

et al. 2020

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Early identification and classification of pulmonary nodules are essential for improving the survival rates of individuals with lung cancer and are considered to be key requirements for computer-assisted diagnosis. To address this topic, the present study proposed a method for predicting the malignant phenotype of pulmonary nodules based on weighted voting rules. This method used the pulmonary nodule regions of interest as the input data and extracted the features of the pulmonary nodules using the Denoising Auto Encoder, ResNet-18. Moreover, the software also modifies texture and shape features to assess the malignant phenotype of the pulmonary nodules. Based on their classification accuracy (Acc), the different classifiers were assigned to different weights. Finally, an integrated classifier was obtained to score the malignant phenotype of the pulmonary nodules. The present study included training and testing experiments conducted by extracting the corresponding lung nodule image data from the Lung Image Database Consortium-Image Database Resource Initiative. The results of the present study indicated a final classification Acc of 93.10±2.4%, demonstrating the feasibility and effectiveness of the proposed method. This method includes the powerful feature extraction ability of deep learning combined with the ability to use traditional features in image representation.

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Section: Algorithm 1 Weighted Voting Algorithm Based On Classificatiomentioning

confidence: 99%

Ensemble classification for predicting the malignancy level of pulmonary nodules on chest computed tomography images

et al. 2020

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“…Rodrigues et al, [23] suggested the approach of systematical co-occurrence matrix (SCM) classifying nodes as malignant nodules or benign nodules. The SCM technique was applied to eliminate nodular image characteristics and classify them as malignant or benign nodules and their malignancy.…”

Section: Survey About Previous Workmentioning

confidence: 99%

Design of Automatic Lung Nodule Detection System Based on Multi-Scene Deep Learning Framework

Zhang

Kong

2020

IEEE Access

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Nowadays, the efficient identification of the lung nodule greatly leads to the chance of lung cancer risk assessment. Hence, the exact locations of lung nodules are a critical and complicated task. Researchers in this area have been working widely for almost two years. However, previous computer-aided detection (CAD) modules, such as transforming CT, segmenting the lung nodule and extracting the features are mostly complex and time-consuming, because more modules will require the creation of a complete image processing system. In addition, certain state-of-the-art deep learning systems are specified in the database standard. For this purpose, this paper suggests an efficient identification system for lung nodules based on Multi-Scene Deep Learning Framework (MSDLF) by the vesselness filter. A four-channel CNN model is designed to enhance the radiologist's knowledge in the detection of four-stage nodules by integrating two image Scenes. This model can be applied in two different classes. The results show that the Multi-Scene Deep Learning Framework (MSDLF) is efficient for increasing the accuracy and significantly reducing false positives in an enormous amount of image data in the detection of lung nodules. INDEX TERMS Multi-scene deep learning framework, vesselness filter, convolutional neural network (CNN), lung nodules. QINGHAI ZHANG was born in 1965. He received the degree. He is currently a Deputy Chief Physician. In 1986, he joined in imaging diagnosis, for more than 30 years. He successively went to the Jinan Military Region General Hospital, Shandong Provincial Institute of Imaging, and 301 Hospital to study CT / MR diagnostics and CT interventional therapy. In practical work, he is also good at learning and summarizing, is diligent in studying and thinking, dares to challenge traditional theories with innovative thinking, and has accumulated rich clinical and practical experience. On the basis of fully mastering common and frequently-occurring imaging diagnosis, in obstetric diseases, we have our own unique insights. He has authored more than ten national academic articles, including four national core journals and two scientific research achievements. XIAOJING KONG was born in 1979. He received the degree from college. He has participated in 2003, involved in imaging diagnosis, for more than 17 years, and successively studied at the

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“…where G(x, y) is a two-dimensional Gaussian function. Then the Hessian matrix of the pixel (x, y) with the Gaussian filter at scale s is shown in (10) and a function is defined using the eigenvalues of the Hessian matrix for measuring vesselness shown in (11):…”

Section: ) Tubular-like Structure Detectionmentioning

confidence: 99%

“…where V 0 (x, y, s) denotes the response of the filter to pixel (x, y) at scale s; λ i (i = 1, 2) are the eigenvalues of H (x, y, s); R A in (12) is the measure of second-order structures and R B in (13) is the 2D blobness measure accounting for the eccentricity of the second-order ellipse; β and c in (11) are thresholds that control the sensitivity of the vessel filter to the measures R A and R B . The method uses the maximum of filter responses V 0 (x, y, s) at all scales to be the vesselness measure:…”

Section: ) Tubular-like Structure Detectionmentioning

confidence: 99%

“…In general, there are four main stages in a CAD system for lung cancer including image preprocessing, nodule candidate detection, false positives (FPs) reduction and malignancy level classification [11], [12]. In the stage of nodule candidate detection, a considerable number of suspicious nodules are marked out from lung parenchyma of the thorax.…”

Section: Introductionmentioning

confidence: 99%

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A Fast and Efficient CAD System for Improving the Performance of Malignancy Level Classification on Lung Nodules

Wang

Cui

et al. 2020

IEEE Access

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Accurate malignancy level classification of lung nodules can reduce lung cancer mortality rate effectively. In this study, we present a fast and efficient CAD system to improve the performance of nodules malignancy level classification. Firstly, to reduce false positives (FPs), we propose a novel vessel segmentation method which measures vessel likelihood by tubular-like structures discriminating from multiple views. The method can recognize irregular vascular structures robustly and sensitively, and achieve fast vessel segmentation. In addition, a mathematical description for 3D pulmonary entities using neighbor centroids clustering is provided as a fundamental condition for spatial feature extraction. To optimize features extraction, we formulate a gray values cumulative function and a patches selection function based on the mathematical description, to generate axial spatial outline and spatial density distribution samples of the entities, respectively. Then, we use Edge Orientation Histogram (EOH) to extract edge features from the spatial outline and propose a multi-scale path LBP (MSPLBP) to extract the texture feature of the density distribution samples. Finally, the fused EOH and MSPLBP are classified into 6 malignancy levels by three state-of-the-art classifiers. The experimental results show that the vessel segmentation method achieves an average F1_Score of 78.14% and AUC value under PR curves of 0.8149. Moreover, our system reaches an average accuracy of 95.88% and consumes average 176.26 seconds for evaluating a CT set on malignancy level classification. These results indicate that the system can segment vessels exactly, and classify the malignancy level of nodules efficiently. Our system is the potential to be a powerful tool for early diagnosis of lung cancer.

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Health of Things Algorithms for Malignancy Level Classification of Lung Nodules

Cited by 85 publications

References 19 publications

Ensemble classification for predicting the malignancy level of pulmonary nodules on chest computed tomography images

Ensemble classification for predicting the malignancy level of pulmonary nodules on chest computed tomography images

Design of Automatic Lung Nodule Detection System Based on Multi-Scene Deep Learning Framework

A Fast and Efficient CAD System for Improving the Performance of Malignancy Level Classification on Lung Nodules

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