InceptionCapsule: Inception-Resnet and CapsuleNet with self-attention
  for medical image Classification
Elham Sadeghnezhad Sajjad Salem
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 middle layers to generate rich vectors. Extracted
vectors are given to a capsule network for learning, which is equipped with an
attention technique. Kvasir data and BUSI with the GT dataset were used to
evaluate this approach. This model was able to achieve 97.62 accuracies in
5-class classification and also achieved 94.30 accuracies in 8-class
classification on Kvasir. In the BUSI with GT dataset, the proposed approach
achieved accuracy=98.88, Precision=95.34, and F1-score=93.74, which are
acceptable results compared to other approaches in the literature.