Effective Multitier Network Model for MRI Brain Disease Prediction using Learning Approaches

Brain disease prognosis is considered a hot research topic where the researchers intend to predict the clinical measures of individuals using MRI data to evaluate the pathological stage and identifies the progression of the disease. With the lack of incomplete clinical scores, various existing learning-based approaches simply eradicate the score without ground truth score computation. It helps restrict the training data samples with robust and reliable models during the learning process. The major disadvantage of the prior approaches is the adoption of hand-crafted features, as these features are not well-suited for the prediction process. This research concentrates on modelling a weakly supervised multi-tier dense neural network model (ws-MTDNN) for examining the progression of brain disease using the available MRI data. The model helps analyze the incomplete clinical scores. The preliminary ties of the network model initially haul out the distinctive patches from the MRI to extract the global and local structural features (information) and develop a superior multi-tier dense neural network model for task-based image feature extraction and perform prediction in the successive tiers for computing the clinical measures. The loss function is adopted while examining the available individuals even in the absence of ground-truth values. The experimentation is done with the available online Dataset like ADNI-1/2, and the model works effectually with this Dataset compared to other approaches.

@article{Ravinder2022,
  title     = {Effective Multitier Network Model for MRI Brain Disease Prediction using Learning Approaches},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {9},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {N. Ravinder and Moulana Mohammed},
  doi       = {10.14569/IJACSA.2022.0130926},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130926}
}