Feature fusion for lung nodule classification

Farag, A.A.M.; Ali, Asem; Elshazly, Salwa; Farag, Aly A.

doi:10.1007/s11548-017-1626-1

Cited by 22 publications

(16 citation statements)

References 22 publications

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“…They used the LIDC-IDRI database [19] and achieved accuracy and recall score of 97% and 94.4%, respectively. Farag et al [20] utilized the feature fusion concept for lung nodule classification. They extracted three features by using signed distance transform shape-based feature descriptor, multi-resolution Local Binary Pattern (LBP) and Gabor filter.…”

Section: A Traditional Methods For Nodule Classificationmentioning

confidence: 99%

Efficient Lung Nodule Classification Using Transferable Texture Convolutional Neural Network

et al. 2020

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Lung nodules are vital indicators for the presence of lung cancer. An early detection enhances the survival rate of the patient by starting treatment at the right time. The detection and classification of malignancy in Computed Tomography (CT) images is a very time-consuming and difficult task for radiologists which lead the researchers to develop algorithms for Computer-Aided Diagnosis (CAD) systems to mitigate this burden. The performance of CAD systems is continuously improving by using various deep learning techniques for screening of lung cancer. In this paper, we proposed transferable texture Convolutional Neural Networks (CNN) to improve the classification performance of pulmonary nodules in CT scans. An Energy Layer (EL) is incorporated in our scheme, which extracts texture features from the convolutional layer. The inclusion of EL reduces the number of learnable parameters of the network, which further reduces the memory requirements and computational complexity. The proposed model has only three convolutional layers and one EL, instead of pooling layer. Overall proposed CNN architecture comprises of nine layers for automatic feature extraction and classification of pulmonary nodule candidates as malignant or benign. Furthermore, the pre-trained model of proposed CNN is also used to handle the smaller dataset classification problem by using transfer learning. This work has been evaluated on publicly available LIDC-IDRI and the LUNGx Challenge database through different evaluation matrices, such as; the accuracy, specificity, error rate and AUC. The proposed model is trained by six-fold cross-validation and achieved an accuracy score of 96.69% ± 0.72% with only 3.30% ± 0.72% error rate. Whereas, the measured AUC and recall is 99.11% ± 0.45% and 97.19% ± 0.57%, respectively. Moreover, we also tested our proposed technique on the MNIST dataset and achieved state-of-the-art results in terms of accuracy and error rate.

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Section: A Traditional Methods For Nodule Classificationmentioning

confidence: 99%

Efficient Lung Nodule Classification Using Transferable Texture Convolutional Neural Network

et al. 2020

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“…These generic features such as SIFT, SURF, HOG, LBP, and Gabor filters etc. were adopted in References [17,41,43,50,70,71,72,73,74,75,76,94,96,107].…”

Section: Analysis Of Selected Workmentioning

confidence: 99%

“…Some selected papers are based on user-defined features, including shape [29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60], texture [29,33,34,35,36,37,38,39,41,44,45,46,47,48,50,54,59,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,…”

Section: Main Process Introductionmentioning

confidence: 99%

“…Some selected papers are based on generic features, including scale-invariant feature transform (SIFT) [17,70,71,72,96,107], local binary pattern (LBP) [41,70,73,74,76,107], histogram of oriented gradient (HOG) [43,50,70,94,107], Gabor [39,41,72,73,74,107], and speeded-up robust features (SURF) [76,99].…”

Section: Main Process Introductionmentioning

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%

See 2 more Smart Citations

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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Segmentation and classification of ground glass nodule on CT images

Wang

Yue

2021

Int J Imaging Syst Tech

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The symptoms of lung cancer mainly manifest as lung nodules. The recognition and diagnosis of a ground glass nodule (GGN) are relatively difficult, and its image features are not easy to extract. To improve the accuracy of segmentation and invasive classification of GGN, a region adaptive Markov random field (MRF) model and a two‐channel integrated network based on densely connected convolutional neural networks (DenseNet) are developed in this paper. First, the lung parenchyma is segmented coarsely, and the corresponding contour is repaired. Afterwards, the fuzzy C‐means (FCM) clustering algorithm and a simple MRF model for the coarse segmentation of GGN are applied. Then, the region adaptive MRF model for the fine segmentation of GGN is used. Moreover, the two‐channel integrated network based on DenseNet is used for GGN invasive classification. Results show that the average value of the overlapping area ratio of the segmentation result to that obtained by one physician was 0.9144, and the accuracy of the proposed GGN invasive classification model, the specificity, sensitivity, and the area under curve were 92.553%, 87.500%, 97.826%, and 0.9715, respectively. The proposed segmentation method can segment GGN more accurately, and the corresponding GGN invasive classification model achieved satisfying classification performance.

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Feature fusion for lung nodule classification

Cited by 22 publications

References 22 publications

Efficient Lung Nodule Classification Using Transferable Texture Convolutional Neural Network

Efficient Lung Nodule Classification Using Transferable Texture Convolutional Neural Network

An Appraisal of Lung Nodules Automatic Classification Algorithms for CT Images

Segmentation and classification of ground glass nodule on CT images

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