Multi-lane LBP-Gabor Capsule Network with K-means Routing for Medical Image Analysis

Medical images naturally occur in smaller quantities and are not balanced. Some medical domains such as radiomics involve the analysis of images to diagnose a patient’s condition. Often, images of sick inaccessible parts of the body are taken for analysis by experts. However, medical experts are scarce, and the manual analysis of the images is time-consuming, costly, and prone to errors. Machine learning has been adopted to automate this task, but it is tedious, time-consuming, and requires experienced annotators to extract features. Deep learning alleviates this problem, but the threat of overfitting on smaller datasets and the existence of the “black box” still lingers. This paper proposes a capsule network that uses Local Binary Pattern (LBP), Gabor layers, and K-Means routing in an attempt to alleviate these drawbacks. Experimental results show that the model produces state-of-the-art accuracy for the three datasets (KVASIR, COVID-19, and ROCT), does not overfit on smaller and imbalanced datasets, and has reduced complexity due to fewer parameters. Layer activation maps, a cluster of features, predictions, and reconstruction of the input images, show that our model is interpretable and has the credibility and trust required to gain the confidence of practitioners for deployment in critical areas such as health.

@article{Mensah2021,
  title     = {Multi-lane LBP-Gabor Capsule Network with K-means Routing for Medical Image Analysis},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {10},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Patrick Kwabena Mensah and Anokye Acheampong Amponsah and Kwame Baffour Agyemang and Gabriel Kofi Armah and Abra Ayidzoe and Faiza Umar Bawah and Adebayor Felix Adekoya and Benjamin Asubam Weyori and Mark Amo-Boateng},
  doi       = {10.14569/IJACSA.2021.0121031},
  url       = {https://doi.org/10.14569/IJACSA.2021.0121031}
}