TITLE:
Application of Convolutional Neural Networks in Classification of GBM for Enhanced Prognosis
AUTHORS:
Rithik Samanthula
KEYWORDS:
Glioblastoma, Machine Learning, Artificial Intelligence, Neural Networks, Brain Tumor, Cancer, Tensorflow, Layers, Cytoarchitecture, Deep Learning, Deep Neural Network, Training, Batches
JOURNAL NAME:
Advances in Bioscience and Biotechnology,
Vol.15 No.2,
February
8,
2024
ABSTRACT: The lethal brain tumor “Glioblastoma” has the propensity to grow over time.
To improve patient outcomes, it is essential to classify GBM accurately and promptly
in order to provide a focused and individualized treatment plan. Despite this, deep
learning methods, particularly Convolutional Neural Networks (CNNs), have demonstrated a high level of accuracy in a myriad of medical
image analysis applications as a result of recent technical breakthroughs.
The overall aim of the research is to investigate how CNNs can be used to classify
GBMs using data from medical imaging, to improve prognosis precision and effectiveness.
This research study will demonstrate a suggested methodology that makes use of the
CNN architecture and is trained using a database of MRI pictures with this tumor.
The constructed model will be assessed based
on its overall performance. Extensive experiments and comparisons with conventional
machine learning techniques and existing classification methods will also be made.
It will be crucial to emphasize the possibility of early and accurate prediction
in a clinical workflow because it can have a big impact on treatment planning and
patient outcomes. The paramount objective is to not only address the classification
challenge but also to outline a clear pathway towards enhancing prognosis precision
and treatment effectiveness.