Research Article
Diagnosis of Glioma, Menigioma and Pituitary brain tumor using MRI images recognition by Deep learning in Python
@ARTICLE{10.4108/eetismla.5410, author={Seyed Masoud Ghoreishi Mokri and Newsha Valadbeygi and Vera Grigoryeva}, title={Diagnosis of Glioma, Menigioma and Pituitary brain tumor using MRI images recognition by Deep learning in Python}, journal={EAI Endorsed Transactions on Intelligent Systems and Machine Learning}, volume={1}, number={1}, publisher={EAI}, journal_a={ISMLA}, year={2024}, month={4}, keywords={Brain tumor, MRI, Detection, Analysis, Deep learning, CNN neural network, Python}, doi={10.4108/eetismla.5410} }- Seyed Masoud Ghoreishi Mokri
Newsha Valadbeygi
Vera Grigoryeva
Year: 2024
Diagnosis of Glioma, Menigioma and Pituitary brain tumor using MRI images recognition by Deep learning in Python
ISMLA
EAI
DOI: 10.4108/eetismla.5410
Abstract
Medical image processing is a very difficult and new field. One thing they do in this field is analyze pictures of people's brains to look for signs of tumors. They use a special computer program to help with this. This paper talks about a new way to use the program to find brain cancer early by looking at the texture of the tumor. This paper explains how we can find and understand brain tumors using special pictures called MRI scans. We use computer programs to help us do this. First, we find the tumor, then we separate it from the rest of the brain, and finally we measure how big it is. We can also figure out how serious the tumor is by looking at different kinds of tumors. To make it easier for people to use, we made a special program in a computer language called COLAB for python codes about using CNN network for deep learning. We tested this program on 8 patients and learned a lot about their tumors.
Copyright © 2024 S. M. Ghoreishi Mokri et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
