Texton Tri-alley Separable Feature Merging (TTSFM) Capsule Network for Brain Tumor Detection

Brain tumors represent one of the most perilous and lethal forms of tumors in both children and adults. Early detection and treatment of such malignant disease types may reduce the mortality rate. However, manual procedures can be used to diagnose such disorders, and this process necessitates a careful, in-depth analysis which is prone to errors, tedious for health professionals, and time-consuming. Therefore, this research aims to design a Texton Tri-alley Separable Feature Merging (TSFM) Capsule Network based on dynamic routing, suitable for the automatic detection of brain tumors. The TSFM Capsule Network’s Texton layer helps to extract important features from the input image, and the separable convolutions coupled with the use of fewer filters and kernel sizes help to reduce the time for training, the size of the model on disk, and the number of trainable parameters generated by the model. The model’s evaluation results on the brain tumor dataset consisting of four classes show better performance than the traditional capsule network, and are comparable to the state-of-the-art models, with an overall accuracy of 97.64%, specificity of 99.24%, precision of 97.43%, sensitivity of 97.45%, f1-score of 97.44%, ROC rate of 99.50%, PR rate of 99.00%. The components and properties of the proposed model make the model deployable on devices with low memory like mobile devices. This model with better performance can assist physicians in the diagnosis of brain tumors.

@article{Akoto-Adjepong2023,
  title     = {Texton Tri-alley Separable Feature Merging (TTSFM) Capsule Network for Brain Tumor Detection},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {9},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Vivian Akoto-Adjepong and Obed Appiah and Peter Appiahene and Patrick Kwabena Mensah},
  doi       = {10.14569/IJACSA.2023.0140951},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140951}
}