A Convolutional Neural Network with a U-Net for Brain Tumor Segmentation and Classification

Steffi. R; Shynu T; S. Suman Rajest; R. Regin

doi:10.17605/cajmns.v4i6.2234

Steffi. R Assistant Professor, Department of Electronics and Communication, Vins Christian College of Engineering, Tamil Nadu, India
Shynu T Master of Engineering, Department of Biomedical Engineering, Agni College of Technology, Chennai, Tamil Nadu, India
S. Suman Rajest Professor, Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India
R. Regin Assistant Professor, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, India

DOI: https://doi.org/10.17605/cajmns.v4i6.2234

Keywords: U-Net Segmentation, FCNN, Convolutional Neural Network, Sigmoid Activation

Abstract

An aberrant proliferation of live brain cells is known as a brain tumour. The development of this tumour within the skull disrupts regular brain function. Early detection is key when it comes to brain tumours; else, they can be fatal. The intricate anatomy of the brain, which can differ from one individual to another, makes tumour detection a difficult undertaking. One possible use of magnetic resonance (MR) imaging is the detection of brain tumours. It can be a tedious and time-consuming operation to accurately segregate tumour areas, though. Accurate tumour segmentation from a given brain MR picture is achieved by our programme using PyTorch, an AI and computer vision library for Python, in conjunction with a CNN (Convolutional Neural Network) based on U-nets. A second Fully Convolutional Neural Network is employed to categorise the tumour into one of its three main types meningioma, glioma, and pituitary tumor—based on the measured tumour area, intensity, form, and location.

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