NROI based feature learning for automated tumor stage classification of pulmonary lung nodules using deep convolutional neural networks

Suresh, Supriya; Mohan, Subaji

doi:10.1016/j.jksuci.2019.11.013

Cited by 25 publications

(12 citation statements)

References 22 publications

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“…In this study, considering the factors that were found to be effective on the 2D CNN parameter design, the parameters with eight factors and three levels are listed in Table 1. To reduce the number of experiments and improve the reliability of experiments, the L 36 (2 11 ,3 12 ) OA was selected for the experimental design, and 36 experimental runs generated by Minitab ® 19 (Scientific Formosa Inc, Taipei, Taiwan) are given in Table 2.…”

Section: Taguchi Methodsmentioning

confidence: 99%

“…Two-dimensional (2D) CNN has been applied in various fields with significant results such as image classification, face recognition, and natural language processing [10,11]. The design of the existing CAD requires the training of a large number of parameters, but parameters setting is complicated, so the parameters must be optimized to increase the accuracy of the classification [12]. Yunus and Alsoufi [13] applied the Taguchi method to evaluate the output quality characteristics to predict the optimum parameters.…”

Section: Introductionmentioning

confidence: 99%

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Using 2D CNN with Taguchi Parametric Optimization for Lung Cancer Recognition from CT Images

2020

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Lung cancer is one of the common causes of cancer deaths. Early detection and treatment of lung cancer is essential. However, the detection of lung cancer in patients produces many false positives. Therefore, increasing the accuracy of the classification of diagnosis or true detection by computed tomography (CT) is a difficult task. Solving this problem using intelligent and automated methods has become a hot research topic in recent years. Hence, we propose a 2D convolutional neural network (2D CNN) with Taguchi parametric optimization for automatically recognizing lung cancer from CT images. In the Taguchi method, 36 experiments and 8 control factors of mixed levels were selected to determine the optimum parameters of the 2D CNN architecture and improve the classification accuracy of lung cancer. The experimental results show that the average classification accuracy of the 2D CNN with Taguchi parameter optimization and the original 2D CNN in lung cancer recognition are 91.97% and 98.83% on the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset, and 94.68% and 99.97% on the International Society for Optics and Photonics with the support of the American Association of Physicists in Medicine (SPIE-AAPM) dataset, respectively. The proposed method is 6.86% and 5.29% more accurate than the original 2D CNN on the two datasets, respectively, proving the superiority of proposed model.

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Section: Taguchi Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using 2D CNN with Taguchi Parametric Optimization for Lung Cancer Recognition from CT Images

2020

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“…A CNN based on deep learning networks learns a hierarchy of increasingly complex features by successive convolution, pooling, and nonlinear activation operations [27,28]. This study designed the architecture of a CNN based on the LeNet network structure including an input three-layer convolutional layer, a two-layer max-pooling layer, a fully connected layer, and final classification.…”

Section: The Cnn Architecturementioning

confidence: 99%

Optimization of Deep Learning Network Parameters Using Uniform Experimental Design for Breast Cancer Histopathological Image Classification

Lin

Jeng

2020

Diagnostics

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Breast cancer, a common cancer type, is a major health concern in women. Recently, researchers used convolutional neural networks (CNNs) for medical image analysis and demonstrated classification performance for breast cancer diagnosis from within histopathological image datasets. However, the parameter settings of a CNN model are complicated, and using Breast Cancer Histopathological Database data for the classification is time-consuming. To overcome these problems, this study used a uniform experimental design (UED) and optimized the CNN parameters of breast cancer histopathological image classification. In UED, regression analysis was used to optimize the parameters. The experimental results indicated that the proposed method with UED parameter optimization provided 84.41% classification accuracy rate. In conclusion, the proposed method can improve the classification accuracy effectively, with results superior to those of other similar methods.

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“…This study, therefore, aims to classify lung cancer based on LUNGX SPIE AAPM data using fuzzy C-Means and fuzzy kernel C-Means clustering algorithm. In addition, previous research on the classification of lung nodules was performed with various methods such as convolutional neural network [8]- [10], support vector machine [11], and semi-supervised adversarial model [12]. The fuzzy C-Means method was initially used to classify thalassemia data [13], breast cancer [14], and intrusion detection system [15], while the fuzzy kernel C-Means was for chronic sinusitis [16], insolvency prediction [17], direction and indonesian stock price movement [18].…”

Section: Introductionmentioning

confidence: 99%

Lung cancer classification using fuzzy c-means and fuzzy kernel C-Means based on CT scan image

Rustam

Purwanto

Hartini

et al. 2021

IJ-AI

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<span id="docs-internal-guid-94842888-7fff-2ae1-cd5c-026943b95b7f"><span>Cancer is one of the diseases with the highest mortality rate in the world. Cancer is a disease when abnormal cells grow out of control that can attack the body's organs side by side or spread to other organs. Lung cancer is a condition when malignant cells form in the lungs. To diagnose lung cancer can be done by taking x-ray images, CT scans, and lung tissue biopsy. In this modern era, technology is expected to help research in the field of health. Therefore, in this study feature extraction from CT images was used as data to classify lung cancer. We used CT scan image data from SPIE-AAPM Lung CT challenge 2015. Fuzzy C-Means and fuzzy kernel C-Means were used to classify the lung nodule from the patient into benign or malignant. Fuzzy C-Means is a soft clustering method that uses Euclidean distance to calculate the cluster center and membership matrix. Whereas fuzzy kernel C-Means uses kernel distance to calculate it. In addition, the support vector machine was used in another study to obtain 72% average AUC. Simulations were performed using different k-folds. The score showed fuzzy kernel C-Means had the highest accuracy of 74%, while fuzzy C-Means obtained 73% accuracy. </span></span>

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NROI based feature learning for automated tumor stage classification of pulmonary lung nodules using deep convolutional neural networks

Cited by 25 publications

References 22 publications

Using 2D CNN with Taguchi Parametric Optimization for Lung Cancer Recognition from CT Images

Using 2D CNN with Taguchi Parametric Optimization for Lung Cancer Recognition from CT Images

Optimization of Deep Learning Network Parameters Using Uniform Experimental Design for Breast Cancer Histopathological Image Classification

Lung cancer classification using fuzzy c-means and fuzzy kernel C-Means based on CT scan image

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