Classification of Thyroid Ultrasound Standard Plane Images using ResNet-18 Networks

Guo, Minghui; Du, Yongzhao

doi:10.1109/icasid.2019.8925267

Cited by 47 publications

(24 citation statements)

References 8 publications

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“…Comparison to State-of-the-Art Approaches. We compared our proposed PSSPNN method with 9 state-of-theart methods: RCBO [7], 6L-CNN [8], RN-50 [9], RN-18 [10], CSSNet [11], COVNet [12], DeCovNet [13], 7L-CNN-CD [14], and FCONet [15]. All the comparison was carried on the same test set of 10 runs.…”

Section: 3mentioning

confidence: 99%

“…In the future, the explainability of our proposed AI model can be used in patient monitoring [38] and health big data [39]. Some novel network improvement and signal 60 RCBO [7] 6L-CNN [8] CSSNet [11] COVNet [12] DeCovNet [13] 7L-CCD [14] FCONet [15] PSSPNN(Ours) RN-50 [9] RN-18 [10] MAF1 (%) 15 Computational and Mathematical Methods in Medicine processing techniques may help our AI model in future researches, such as filters [40,41], fuzzy [42,43], edge computing [44], knowledge-aid [45,46], autofocus [47], graph integration, and cross-domain knowledge exploitation [48][49][50].…”

Section: 3mentioning

confidence: 99%

“…The authors found that ResNet-50 models help identify AD patients. Guo and Du [10] utilized a ResNet-18 (RN-18) model to recognize thyroid ultrasound standard plane (TUSP), achieving a classification accuracy of 83.88%. The experiments verified the effectiveness of RN-18.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation

Wang

Zhang

Cheng

et al. 2021

Computational and Mathematical Methods in Medicine

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Aim. COVID-19 has caused large death tolls all over the world. Accurate diagnosis is of significant importance for early treatment. Methods. In this study, we proposed a novel PSSPNN model for classification between COVID-19, secondary pulmonary tuberculosis, community-captured pneumonia, and healthy subjects. PSSPNN entails five improvements: we first proposed the n-conv stochastic pooling module. Second, a novel stochastic pooling neural network was proposed. Third, PatchShuffle was introduced as a regularization term. Fourth, an improved multiple-way data augmentation was used. Fifth, Grad-CAM was utilized to interpret our AI model. Results. The 10 runs with random seed on the test set showed our algorithm achieved a microaveraged F1 score of 95.79%. Moreover, our method is better than nine state-of-the-art approaches. Conclusion. This proposed PSSPNN will help assist radiologists to make diagnosis more quickly and accurately on COVID-19 cases.

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Section: 3mentioning

confidence: 99%

Section: 3mentioning

confidence: 99%

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PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation

Wang

Zhang

Cheng

et al. 2021

Computational and Mathematical Methods in Medicine

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“…We compared our DBDRMBN method, i.e., N (8) model, with 15 state-of-the-art approaches: RBFNN [4] , KELM [5] , ELM-BA [6] , RCBBO [7] , 6L-CLF [8] , GoogLeNet [9] , ResNet-18 [10] , RN-50-AD [11] , SMO [12] , CSSNet [13] , GGNet [14] , COVNet [15] , NiNet [16] , FCONet [17] , and DeCovNet [18] . All the methods were compared on the test set of our 640-image dataset.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Szegedy, et al [9] presented the GoogleNet. Guo and Du [10] suggested the use of ResNet-18 for thyroid ultrasound classification. Fulton, et al [11] employed ResNet-50 for classification of Alzheimer's disease.…”

Section: Introductionmentioning

confidence: 99%

Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network

et al. 2021

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Highlights We analysed over 320 COVID-19 images and 320 healthy control images. We proposed an improved CNN to extract individual image-level features. We proposed to use GCN to extract relation-aware representations. We proposed a DFF technology to combine features from GCN and CNN. The proposed FCGNet gives better performance than 15 state-of-the-art approaches.

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Attention feature fusion methodology with additional constraint for ovarian lesion diagnosis on magnetic resonance images

Wang

et al. 2022

Medical Physics

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It is challenging for radiologists and gynecologists to identify the type of ovarian lesions by reading magnetic resonance (MR) images. Recently developed convolutional neural networks (CNNs) have made great progress in computer vision, but their architectures still need modification if they are used in processing medical images. This study aims to improve the feature extraction capability of CNNs, thus promoting the diagnostic performance in discriminating between benign and malignant ovarian lesions. Methods: We introduce a feature fusion architecture and insert the attention models in the neural network.The features extracted from different middle layers are integrated with reoptimized spatial and channel weights. We add a loss function to constrain the additional probability vector generated from the integrated features, thus guiding the middle layers to emphasize useful information. We analyzed 159 lesions imaged by dynamic contrast-enhanced MR imaging (DCE-MRI), including 73 benign lesions and 86 malignant lesions. Senior radiologists selected and labeled the tumor regions based on the pathology reports. Then, the tumor regions were cropped into 7494 nonoverlapping image patches for training and testing. The type of a single tumor was determined by the average probability scores of the image patches belonging to it. Results: We implemented fivefold cross-validation to characterize our proposed method, and the distribution of performance matrics was reported. For all the test image patches, the average accuracy of our method is 70.5% with an average area under the curve (AUC) of 0.785, while the baseline is 69.4% and 0.773, and for the diagnosis of single tumors, our model achieved an average accuracy of 82.4% and average AUC of 0.916, which were better than the baseline (81.8% and 0.899). Moreover, we evaluated the performance of our proposed method utilizing different CNN backbones and different attention mechanisms. Conclusions:The texture features extracted from different middle layers are crucial for ovarian lesion diagnosis. Our proposed method can enhance the feature extraction capabilities of different layers of the network, thereby improving diagnostic performance.

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Classification of Thyroid Ultrasound Standard Plane Images using ResNet-18 Networks

Cited by 47 publications

References 8 publications

PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation

PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation

Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network

Attention feature fusion methodology with additional constraint for ovarian lesion diagnosis on magnetic resonance images

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