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

arXiv: https://arxiv.org/abs/2402.02274 · HTML: https://mlsanity.com/paper/2402.02274/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 59 references that are in the map.
- First posted on arXiv 3 Feb 2024.

## Builds on (most cited first, 9 of 9)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [Attention Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound Images](https://mlsanity.com/paper/1910.08978/) (2019)
- [A CNN-based methodology for breast cancer diagnosis using thermal images](https://mlsanity.com/paper/1910.13757/) (2019)
- [Fuzzy Semantic Segmentation of Breast Ultrasound Image with Breast Anatomy Constraints](https://mlsanity.com/paper/1909.06645/) (2019)

## Built on by (most cited first, 1 of 1)

- [A Comprehensive Analysis on Machine Learning based Methods for Lung Cancer Level Classification](https://mlsanity.com/paper/2501.18294/) (2025)

## Abstract (arXiv)

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.