InceptionCapsule: Inception-Resnet and CapsuleNet with self-attention for medical image Classification

Initial weighting is significant in deep neural networks because the random selection of weights produces different outputs and increases the probability of overfitting and underfitting. On the other hand, vector-based approaches to extract vector features need rich vectors for more accurate classification. The InceptionCapsule approach is presented to alleviate these two problems. This approach uses transfer learning and the Inception-ResNet model to avoid random selection of weights, which takes initial weights from ImageNet. It also uses the output of Inception …

attention classification cs.ai cs.cv cs.lg extract features image medical networks neural networks overfitting probability random resnet self-attention underfitting vector vectors