Pulmonary nodules detection algorithm based on robust cascade classifier for CT images

Li, Xia; Yang, Yang; Huagang, Xiong; Song, Shangling; Jia, Hongying

doi:10.1109/ccdc.2017.7978097

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…In the final model, Acc, Pre and Sen reached 93.10, 83.85 and 81.75%, respectively. The overall performance was higher than that of state-of-the-art methods (13,(29)(30)(31)(32).…”

Section: Discussionmentioning

confidence: 73%

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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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“…In the final model, Acc, Pre and Sen reached 93.10, 83.85 and 81.75%, respectively. The overall performance was higher than that of state-of-the-art methods (13,(29)(30)(31)(32).…”

Section: Discussionmentioning

confidence: 73%

“…Using parallel fusion, weighted voting was based on the error rate (30)(31)(32). The ensemble classification model can utilize the features of each classifier and further ensure flexibility between the different feature coefficients of each classifier (Table I).…”

Section: Pulmonary Nodule Classification By Handcrafted Featuresmentioning

confidence: 99%

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

et al. 2020

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Pulomonary Nodule Classification From CT Scan Images Using Machine Learning Method

Rekha

Itagi

Bharath

et al. 2021

Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments

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The research work is to enhance the classification accuracy of the pulmonary nodules with the limited number of features extracted using Gray level co-occurrence matrix and linear binary pattern. The classification is done using the machine learning algorithm such as artificial neural network (ANN) and the random forest classifier (RF). In present, lung cancer seems to be the most deadly disease in the world which can be detected only after the computerized tomography (i.e., CT scan images of the person). Detecting the infected portion at the early period is the challenging task. Hence, the recent researchers where under the detection of pulmonary nodules to categorize it either as benign nodules which named as non-cancerous or as malignant nodules which are named as cancerous. When associated the results with the recent papers, the accuracy has been improved in classifying the lung nodules.

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Pulmonary nodules detection algorithm based on robust cascade classifier for CT images

Cited by 2 publications

References 8 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

Pulomonary Nodule Classification From CT Scan Images Using Machine Learning Method

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