Brain Tumour Classification Using Deep Learning
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Date
2022-09-19
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Publisher
university of M'sila
Abstract
A brain tumour is a fatal disease affects children and adults the disease might be detected using
physical exam, neurological exam but for the classification, it is done with biopsy. That last one
is concerned with brain surgery, which is so hard and complicated itself. Nowadays it is so
important for the early detection because of the five-year rate of survival.
The early detection and classification could help to choose the perfect plan for treatment. With
the big development and change in technology and AI techniques could help in diagnosis and
classification without any huge risks, Using the available data of MRI images that are studied
from the radiologist.
In our study, we took two approaches, the first including four transfer learning models and the
second including a CNN model, to both classify different types of brain tumour. Using three
different datasets available at kaggle
With the CNN approach, we manage to achieve an accuracy of 99.76 %. The Experimental
Results shows that our proposed Convolution Neural Network model (CNN) gives the best
accuracy as compared to other transfer learning techniques. At last, we make a small
comparison with the state of the art methods.