Distributed Networks for Brain Tumor Classification Through Temporal Learning and Hybrid Attention Segmentation

Brain Tumor (BT), which is the progress of abnormal cells in brain surface is categorized into different types based on the symptoms and the affected parts in brain. Classification of BT using Magnetic Resonance Imaging (MRI) is an important and challenging task for BT diagnosis. Various approaches are designed to solve the issues and there are so many inconsistencies in detecting the tumor at early stage. The changes in variability and the complexity of size, shape, location and texture of lesions, automatic detection of BT still results a challenging task in the medical research community. Hence, a proposed Hybrid Attention Temporal Difference Learning with Distributed Convolutional Neural Network-Bidirectional Long Short-Term Memory (HATDL-DCNN-BiLSTM) is developed in this research to detect and classify the BT at beginning stage that enables to improve the survival rate of humans. The proposed model uses Gaussian filter for input image enhancement, Hybrid Attention-VNet segmentation to generate region of interest and solves the computational issues through the attention modules by minimizing the dimensions. The proposed model consumed less memory utilization and increase the training speed globally using the distributed learning mechanism. The features extracted using Hybrid Attention based Efficient Statistical Triangular ResNet (HA-ESTER) supports the classification model to increase the training efficiency more accurately. The proposed HATDL-DCNN-BiLSTM attains higher efficiency by the metrics of accuracy, recall, F1-score, and precision of 98.93%, 99.21%, 97.67%, and 96.17% with training data, and accuracy, recall, F1-score, and precision of 96.34%, 96.51%, 96.33%, and 96.15% with k-fold using BraTS 2019 dataset.

@article{Pathan2024,
  title     = {Distributed Networks for Brain Tumor Classification Through Temporal Learning and Hybrid Attention Segmentation},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {12},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Sayeedakhanum Pathan and Savadam Balaji},
  doi       = {10.14569/IJACSA.2024.0151242},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151242}
}