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  1. 321
    “…Our experimental results of the supervised histopathological image classification of breast cancer and the comparison to the results from other studies demonstrate that Inception_V3 and Inception_ResNet_V2 based histopathological image classification of breast cancer is superior to the existing methods. …”
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    Online Artículo Texto
    Deep Learning Based Analysis of Histopathological Images of Breast Cancer
  2. 322
    “…We searched the following databases: AMED (inception to June 2005), Campbell Collaboration (inception to June 2005), CinAhl (inception to June 2005), Cochrane Library (inception to June 2005), Embase (inception to June 2005), ERIC (inception to June 2005), MedLine (inception to June 2005), and NHS EED (inception to June 2005). …”
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    Adherence to HAART: A Systematic Review of Developed and Developing Nation Patient-Reported Barriers and Facilitators
  3. 323
    “…Two-stream convolutional network models based on deep learning were proposed, including inflated 3D convnet (I3D) and temporal segment networks (TSN) whose feature extraction network is Residual Network (ResNet) or the Inception architecture (e.g., Inception with Batch Normalization (BN-Inception), InceptionV3, InceptionV4, or InceptionResNetV2) to achieve pig behavior recognition. …”
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    Automated Video Behavior Recognition of Pigs Using Two-Stream Convolutional Networks
  4. 324
    por Pang, Xiaojia
    Publicado 2022
    “…Among them, the number of interactive learning elements inception modules used by the network models GoogLeNet, Inception-v2, Inception-v4, and Inception-ResNet-v2 are 9, 10, 14, and 20, respectively. …”
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    Intelligent Psychology Teaching System Based on Adaptive Neural Network
  5. 325
    “…Then, multiple transfer learning models, such as Resnet50, InceptionV3, and Inception Resnet, were used for fine-tuning. …”
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    Detection of Skin Cancer Based on Skin Lesion Images Using Deep Learning
  6. 326
    “…Inception-ResNet-V2 achieved the highest AUC (97.62%) similar to the model ensemble, followed by InceptionV3 (AUC of 96.82%) and VGG-16 (AUC 96.03%). …”
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    Deep Learning for the Differential Diagnosis between Transient Osteoporosis and Avascular Necrosis of the Hip
  7. 327
    “…Similarly, the GWO-DenseNet, GWO-InceptionV3 and GWO-VGG16 + InceptionV3 models result an AUC-ROC score of 100 for SPECT DaTscan dataset.…”
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    An Improved Method for Diagnosis of Parkinson’s Disease using Deep Learning Models Enhanced with Metaheuristic Algorithm
  8. 328
    “…We trained cross-validated ResNet50, DenseNet201, InceptionV3, and InceptionResNetV2 models on 1150 pediatric tympanic membrane images from otoendoscopes to classify OME. …”
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    Improving the Accuracy of Otitis Media with Effusion Diagnosis in Pediatric Patients Using Deep Learning
  9. 329
    “…In a nutshell, this research employed an Inception-V3 and InceptionResnet-V2 strategy for melanoma recognition. …”
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    Diagnosing Melanomas in Dermoscopy Images Using Deep Learning
  10. 330
    “…Simultaneously, the InceptionTime model network shows its potential in dealing with data imbalances.…”
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    Deep Learning Approach for Damage Classification Based on Acoustic Emission Data in Composite Materials
  11. 331
    “…The categorized images were then processed in a training, validation, and test run by the ImageNet pretrained CNN frameworks (Inception ResNet v2, Inception v3, ResNet152, Xception) in different pixel sizes. …”
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    Ein Vergleich von 4 konvolutionalen neuronalen Netzen in der histopathologischen Diagnostik von Speicheldrüsenkarzinomen
  12. 332
    “…This paper investigates the classification of radiographic images with eleven convolutional neural network (CNN) architectures (GoogleNet, VGG-19, AlexNet, SqueezeNet, ResNet-18, Inception-v3, ResNet-50, VGG-16, ResNet-101, DenseNet-201 and Inception-ResNet-v2). …”
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    Classification and Visualisation of Normal and Abnormal Radiographs; A Comparison between Eleven Convolutional Neural Network Architectures
  13. 333
    “…RESULTS: from the results of all 12 pre-trained deep learning model, five models that have highest validation accuracy were DenseNet169, DenseNet201, InceptionV3, DenseNet121 and InceptionRestNetV2, respectively. …”
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    Comparison of pre-trained deep learning model classification performance of COVID-19 and normal chest X-ray images
  14. 334
    “…The main mechanism in RISE-Net is the stacked Residual, Inception, Squeeze and Excitation (RISE) blocks. This classification network achieved an accuracy of 90.34% and a F1 score of 90.39% and outperformed other state-of-the-art architectures, such as VGG-16, Inception, ResNet-50, ReNet-Inception, SE-ResNet, and SE-Inception. …”
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    Online Artículo Texto
    Automatic Artifact Detection Algorithm in Fetal MRI
  15. 335
    “…The highest classification accuracy of 97.57% was achieved by the Inception-v3 model based on the T2WI data. In addition, Inception-v3 performed statistically significantly better than the Alexnet architecture (p<0.05). …”
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    Differentiation of Glioma Mimicking Encephalitis and Encephalitis Using Multiparametric MR-Based Deep Learning
  16. 336
    “…We divided the lesions and images into training and testing sets at a ratio of 7: 3. The Inception V3 model was pretrained by the training dataset. …”
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    A Novel Deep Learning Model to Distinguish Malignant Versus Benign Solid Lung Nodules
  17. 337
    “…The GAN with nonlinear identity blocks achieved an inception score of 14.32 and a Fréchet inception distance of 41.86 in the augmenting process. …”
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    Breast Ultrasound Images Augmentation and Segmentation Using GAN with Identity Block and Modified U-Net 3+
  18. 338
    “…Training strategies include training the last layer of Google's Inceptions, training the network from scratch, and fine-tunning the parameters for our data using two pre-trained version of Google's Inception architectures, Inception-V1 and Inception-V3. …”
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    Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images
  19. 339
    “…Untrained and pretrained deep convolutional neural network models for Inception V3, ResNet50, and DenseNet 121 were each employed. …”
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    Assessment of Critical Feeding Tube Malpositions on Radiographs Using Deep Learning
  20. 340
    “…DenseNet121 and VGG16 attained a sensitivity of 99.94%, while InceptionV3 and InceptionResNetV2 achieved a specificity of 100%. …”
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    COVID-19 diagnosis using deep learning neural networks applied to CT images
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