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anindox8 / Ensemble-of-Multi-Scale-CNN-for-Dermatoscopy-Classification

Star 44

Fully supervised binary classification of skin lesions from dermatoscopic images using an ensemble of diverse CNN architectures (EfficientNet-B6, Inception-V3, SEResNeXt-101, SENet-154, DenseNet-169) with multi-scale input.

deep-learning ensemble-learning densenet convolutional-neural-networks binary-classification inception-v3 senet multi-scale seresnext cyclic-learning-rate skin-lesion-classification shades-of-gray tensorflow-keras color-constancy efficientnet
  • Updated Oct 27, 2020
  • Jupyter Notebook
TF-1D-2D-ResNetV1-2-SEResNet-ResNeXt-SEResNeXt

Sakib1263 / TF-1D-2D-ResNetV1-2-SEResNet-ResNeXt-SEResNeXt

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Models supported: ResNet, ResNetV2, SE-ResNet, ResNeXt, SE-ResNeXt [layers: 18, 34, 50, 101, 152] (1D and 2D versions with DEMO for Classification and Regression).

demo machine-learning deep-learning pipeline jupyter-notebook python3 resnet regression-models resnext 2d-convolution keras-tensorflow classification-model 1d-convolution seresnet seresnext tensorflow2
  • Updated Jan 27, 2022
  • Jupyter Notebook

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