Design and Analysis of Deep Learning Method for Fragmenting Brain Tissue in MRI Images

An essential component of medical image processing is brain tumour segmentation. The process of giving each pixel a label is called image segmentation in order for pixels bearing the same label to share characteristics and help distinguish the target. A higher fatality rate and additional dangers can be avoided with early identification. It can be challenging and time-consuming to manually (man-made) segment brain tumours from the numerous MRI pictures generated during medical procedures in order to diagnose malignancy. This is the fundamental reason why brain tumour imaging has to be automated. The deep learning technique for the segmentation of brain tissue in magnetic resonance imaging (MRI) pictures was examined and enhanced in this work. Researchers are using deep learning techniques—convolutional neural networks in particular—to tackle the complex problem of biological image fragmentation object recognition. In contrast to traditional classification techniques that take in manually constructed qualities, convolutional neural networks automatically extract the required complicated features from the data itself. This solves a number of problems.

@article{Yang2024,
  title     = {Design and Analysis of Deep Learning Method for Fragmenting Brain Tissue in MRI Images},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {1},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Ting Yang and Jiabao Sun},
  doi       = {10.14569/IJACSA.2024.0150110},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150110}
}