Three-Dimensional ResNeXt Network Using Feature Fusion and Label Smoothing for Hyperspectral Image Classification

Wu, Peida; Cui, Ziguan; Gan, Zongliang; Liu, Feng

doi:10.3390/s20061652

Cited by 21 publications

(14 citation statements)

References 44 publications

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“…We compared the diagnostic performance of our model with the four machine learning algorithms which are currently most popular and advanced, including ResNeXt [14], SE_Inception_v4 [15], SE_Net [16] and Xception [17]. These models are widely used in the field of AI of medical images [18,19]. The 3000 ultrasound images randomly selected from the test set in Center 1 were used for this part of the study.…”

Section: Comparison With Four State-of-the-art Deep Learning Modelsmentioning

confidence: 99%

Ensemble Deep Learning Model for Multicenter Classification of Thyroid Nodules on Ultrasound Images

Wei

Gao

et al. 2020

Med Sci Monit

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Background:Thyroid nodules are extremely common and typically diagnosed with ultrasound whether benign or malignant.Imaging diagnosis assisted by Artificial Intelligence has attracted much attention in recent years. The aim of our study was to build an ensemble deep learning classification model to accurately differentiate benign and malignant thyroid nodules. Material/Methods:Based on current advanced methods of image segmentation and classification algorithms, we proposed an ensemble deep learning classification model for thyroid nodules (EDLC-TN) after precise localization. We compared diagnostic performance with four other state-of-the-art deep learning algorithms and three ultrasound radiologists according to ACR TI-RADS criteria. Finally, we demonstrated the general applicability of EDLC-TN for diagnosing thyroid cancer using ultrasound images from multi medical centers. Results:The method proposed in this paper has been trained and tested on a thyroid ultrasound image dataset containing 26 541 images and the accuracy of this method could reach 98.51%. EDLC-TN demonstrated the highest value for area under the curve, sensitivity, specificity, and accuracy among five state-of-the-art algorithms.Combining EDLC-TN with models and radiologists could improve diagnostic accuracy. EDLC-TN achieved excellent diagnostic performance when applied to ultrasound images from another independent hospital. Conclusions:Based on ensemble deep learning, the proposed approach in this paper is superior to other similar existing methods of thyroid classification, as well as ultrasound radiologists. Moreover, our network represents a generalized platform that potentially can be applied to medical images from multiple medical centers.

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Section: Comparison With Four State-of-the-art Deep Learning Modelsmentioning

confidence: 99%

Ensemble Deep Learning Model for Multicenter Classification of Thyroid Nodules on Ultrasound Images

Wei

Gao

et al. 2020

Med Sci Monit

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“…Cao and Guo further introduced hybrid dilated convolutions (HDC) and the residual block based on SSRN and proposed a new end-to-end hybrid expansion residual deep convolutional network [32]. Wu et al designed the 3D ResNeXt structure using feature fusion and label smoothing strategies [33].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Depth-Separable Residual Networks for Hyperspectral Image Classification

Zhao

Wang

et al. 2020

Complexity

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At present, the classification of the hyperspectral image (HSI) based on the deep convolutional network has made great progress. Due to the high dimensionality of spectral features, limited samples of ground truth, and high nonlinearity of hyperspectral data, effective classification of HSI based on deep convolutional neural networks is still difficult. This paper proposes a novel deep convolutional network structure, namely, a hybrid depth-separable residual network, for HSI classification, called HDSRN. The HDSRN model organically combines 3D CNN, 2D CNN, multiresidual network ROR, and depth-separable convolutions to extract deeper abstract features. On the one hand, due to the addition of multiresidual structures and skip connections, this model can alleviate the problem of over fitting, help the backpropagation of gradients, and extract features more fully. On the other hand, the depth-separable convolutions are used to learn the spatial feature, which reduces the computational cost and alleviates the decline in accuracy. Extensive experiments on the popular HSI benchmark datasets show that the performance of the proposed network is better than that of the existing prevalent methods.

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“…In 2017, Huang et al [ 9 ] proposed DenseNet, which made it possible for feature reusing and provided another way for feature fusion, which is realized by the concatenation of different feature maps. In recent years, the above mentioned two feature fusion methods, which are proposed in ResNet and DenseNet, that have been widely used in the tasks of image classification [ 10 , 11 ], semantic segmentation [ 12 , 13 ], object detection [ 14 , 15 ], etc. Additionally, they are served as the standard patterns of feature extraction based on CNN.…”

Section: Introductionmentioning

confidence: 99%

Spatial–Spectral Feature Refinement for Hyperspectral Image Classification Based on Attention-Dense 3D-2D-CNN

Zhang

Feng-yuan

Feng

et al. 2020

Sensors

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Convolutional neural networks provide an ideal solution for hyperspectral image (HSI) classification. However, the classification effect is not satisfactory when limited training samples are available. Focused on “small sample” hyperspectral classification, we proposed a novel 3D-2D-convolutional neural network (CNN) model named AD-HybridSN (Attention-Dense-HybridSN). In our proposed model, a dense block was used to reuse shallow features and aimed at better exploiting hierarchical spatial–spectral features. Subsequent depth separable convolutional layers were used to discriminate the spatial information. Further refinement of spatial–spectral features was realized by the channel attention method and spatial attention method, which were performed behind every 3D convolutional layer and every 2D convolutional layer, respectively. Experiment results indicate that our proposed model can learn more discriminative spatial–spectral features using very few training data. In Indian Pines, Salinas and the University of Pavia, AD-HybridSN obtain 97.02%, 99.59% and 98.32% overall accuracy using only 5%, 1% and 1% labeled data for training, respectively, which are far better than all the contrast models.

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Three-Dimensional ResNeXt Network Using Feature Fusion and Label Smoothing for Hyperspectral Image Classification

Cited by 21 publications

References 44 publications

Ensemble Deep Learning Model for Multicenter Classification of Thyroid Nodules on Ultrasound Images

Ensemble Deep Learning Model for Multicenter Classification of Thyroid Nodules on Ultrasound Images

Hybrid Depth-Separable Residual Networks for Hyperspectral Image Classification

Spatial–Spectral Feature Refinement for Hyperspectral Image Classification Based on Attention-Dense 3D-2D-CNN

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