Lung Nodule Classification via Deep Transfer Learning in CT Lung Images

Nóbrega, Raul Victor Medeiros da; Peixoto, Solon Alves; Silva, Suane Pires P. da; Gupta, Deepak

doi:10.1109/cbms.2018.00050

Cited by 107 publications

(57 citation statements)

References 27 publications

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“…The obtained results are 94 %, 98 %, 92 %, and 100 % sensitivity respectively. Victor et al [5] used the CNN-ResNet50 with SVM-RBF and obtained 88.41 % accuracy on LIDC 1536 samples with 80 % training and 20 % testing. The same number of training samples was used with AlexNet, GoogleNet, ResNet50, and the proposed network.…”

Section: Resultsmentioning

confidence: 99%

“…It is also called as True Positive Rate, TPR, and calculated by using Eq. (5). (2) Accuracy: Accuracy is the ratio of a number of correct predictions to the total number of predictions and it is calculated using Eq.…”

Section: Lidc Datasetmentioning

confidence: 99%

“…Victor et al [5] used deep transfer learning and obtained 88.41 % accuracy. Jan et al [6] proposed a lung segmentation method based on morphological and circular filter.…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning techniques have been used to detection and classification of the cancerous lesions in medical images, which can help radiologists make decisions, especially for the cases which are difficult to identify, improving the accuracy with efficiency. The literature survey [2][3][4][5][6][7][8] shows that there is a need to propose a method to improve accuracy by classification. The remaining part of the manuscript is organized as follows: In Section 2 describe the Convolutional Neural Network.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Lung Cancer Detection Based on CT Scan Images by Using Deep Transfer Learning

Sajja¹,

Devarapalli²,

Kalluri³

2019

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Lung cancer is the world's leading cause of cancer death. The convolutional neural network (CNN) has been proved able to classify between malignant and benign tissues on CT scan images. In this paper, a deep neural network is designed based on GoogleNet, a pre-trained CNN. To reduce the computing cost and avoid overfitting in network learning, the densely connected architecture of the proposed network was sparsified, with 60 % of all neurons deployed on dropout layers. The performance of the proposed network was verified through a simulation on a pre-processed CT scan image dataset: The Lung Image Database Consortium (LIDC) dataset, and compared with that of several pre-trained CNNs, namely, AlexNet, GoogleNet and ResNet50. The results show that our network achieved better classification accuracy than the contrastive networks.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Lidc Datasetmentioning

confidence: 99%

“…Victor et al [5] used deep transfer learning and obtained 88.41 % accuracy. Jan et al [6] proposed a lung segmentation method based on morphological and circular filter.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Lung Cancer Detection Based on CT Scan Images by Using Deep Transfer Learning

Sajja¹,

Devarapalli²,

Kalluri³

2019

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show abstract

“…As an important branch of machine learning, deep learning has developed rapidly in recent years. Convolutional neural networks (CNNs) have achieved good results in the field of face recognition, object detection, image classification, and other images, due to a large amount of available data and the efficient computing capacity of GPUs, and it has also been applied to medical images [13,14]. Ronneberger et al [15] reported a new full convolution network (FCN) called U-Net for biomedical image segmentation and achieved promising results.…”

Section: Introductionmentioning

confidence: 99%

Classification of Lung Nodules Based on Deep Residual Networks and Migration Learning

Sun

Zhao

et al. 2020

Computational Intelligence and Neuroscience

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The classification process of lung nodule detection in a traditional computer-aided detection (CAD) system is complex, and the classification result is heavily dependent on the performance of each step in lung nodule detection, causing low classification accuracy and high false positive rate. In order to alleviate these issues, a lung nodule classification method based on a deep residual network is proposed. Abandoning traditional image processing methods and taking the 50-layer ResNet network structure as the initial model, the deep residual network is constructed by combining residual learning and migration learning. The proposed approach is verified by conducting experiments on the lung computed tomography (CT) images from the publicly available LIDC-IDRI database. An average accuracy of 98.23% and a false positive rate of 1.65% are obtained based on the ten-fold cross-validation method. Compared with the conventional support vector machine (SVM)-based CAD system, the accuracy of our method improved by 9.96% and the false positive rate decreased by 6.95%, while the accuracy improved by 1.75% and 2.42%, respectively, and the false positive rate decreased by 2.07% and 2.22%, respectively, in contrast to the VGG19 model and InceptionV3 convolutional neural networks. The experimental results demonstrate the effectiveness of our proposed method in lung nodule classification for CT images.

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A computed tomography signs quantization analysis method for pulmonary nodules malignancy grading

Chen

Xia

Duan

et al. 2021

Int J Imaging Syst Tech

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In order to improve the accuracy of pulmonary nodules malignancy grading, we propose a method to implement quantitative analysis for lung nodules using computed tomography (CT) signs. Firstly, we construct feature sets of CT signs by combing the radiomics features with the higher‐order features extracted from a convolutional neural network. Secondly, on the basis of the mixed feature set, an evolutionary ensemble learning mechanism is used to generate a classifier to get the quantitative scores for seven lung nodule CT signs. Finally, the scores of seven CT signs are input into a multiclassifier optimized by the differential evolution algorithm to acquire the grade of malignancy. In the experimental study, 2000 lung nodule samples from the LIDC‐IDRI dataset were used to train and test the evolutionary ensemble learner and malignancy classifier. The results show that the recognition accuracy of seven CT signs can reach more than 0.964. Comparison with many typical algorithms, the proposed method not only gets higher accuracy in pulmonary nodules malignancy grading but also can make the result more interpretable.

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Lung Nodule Classification via Deep Transfer Learning in CT Lung Images

Cited by 107 publications

References 27 publications

Lung Cancer Detection Based on CT Scan Images by Using Deep Transfer Learning

Lung Cancer Detection Based on CT Scan Images by Using Deep Transfer Learning

Classification of Lung Nodules Based on Deep Residual Networks and Migration Learning

A computed tomography signs quantization analysis method for pulmonary nodules malignancy grading

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