The abnormal growth and division of cells in the brain lead to a brain tumor, and
the
further growth
of brain tumors leads to brain cancer. In the area of human health, Computer Vision
plays a
significant role, which reduces the human judgment that gives accurate results. CT
scans,
X-Ray, and
MRI scans are the common imaging methods among magnetic resonance imaging (MRI) that
are the
most
reliable and secure. We performed pre-processing using the bilateral filter (BF) for
removal
of the
noises that are present in an MR image. This was followed by the binary thresholding
and
Convolution
Neural Network (CNN) segmentation techniques for reliable detection of the tumor
region.
Based on
our machine, we will predict whether the subject has a brain tumor or not.
Medical imaging is the technique and process of creating visual representations of
the
interior of a
body for clinical analysis and medical intervention, as well as visual
representation of the
function of some organs or tissues.This process pursues the disorder identification
and
management.
This process creates a data bank of the regular structure and function of the organs
to make
it easy
to recognize the anomalies.Problem is Brain Tumour vary in size, shape, appearance,
colour,
location
and orientation, which is precisely the reason why tumor segmentation is challenging
Here, we develop a deep learning algorithm that can accurately detect cancer on
screening
using an
“end-to-end” training approach that efficiently leverages training datasets with
either
complete
clinical annotation or only the cancer status (label) of the whole image.
The main goal is to classify the brain in the presence of a brain tumor or a healthy
brain.
