2D Encoding Convolution Neural Network Algorithm for Brain Tumour Prediction
DOI:
https://doi.org/10.12723/mjs.sp2.1Keywords:
Deep Learning, Magnetic Resonance Image (MRI), Convolutional Neural NetworkAbstract
In contemporary times, biomedical imaging plays a pivotal role in addressing various patient-related concerns. Brain imaging, particularly through techniques like MRI, offers valuable insights crucial for surgical procedures, radiotherapy, treatment planning, and stereotactic neurosurgery. To facilitate the accurate identification of cancerous cells within the brain using MRI, deep learning and image classification techniques have been deployed. These technologies have paved the way for the development of automated tumor detection methods, which not only save valuable time for radiologists but also consistently deliver proven levels of accuracy. In contrast, the conventional approach to defect detection in magnetic resonance brain images relies on manual human inspection, a method rendered impractical due to the sheer volume of data This paper outlines an approach aimed at detecting and classifying brain tumors within patient MRI images. Additionally, it conducts a performance comparison of Convolutional Neural Network (CNN) models in this context.
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