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

TL;DR

The paper introduces InceptionCapsule, a novel deep learning model combining Inception-ResNet, capsule networks, and self-attention to improve medical image classification accuracy and robustness.

Contribution

It presents a new architecture that integrates transfer learning, rich vector extraction, and attention mechanisms for enhanced medical image classification.

Findings

Achieved 97.62% accuracy on 5-class Kvasir dataset

Achieved 94.30% accuracy on 8-class Kvasir dataset

Achieved 98.88% accuracy on BUSI dataset with high precision and F1-score

Abstract

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.