Brain Tumor Detection by using ResNet-50 and Image Processing Tools

Tumor is an uncontrollable growth of brain and its surrounding tissues. The probably rate of survival of a person having a tumor depends on various factors such as location, kind, size of tumor and age of patient and overall health. Detection of brain tumors is key to improving patient outcomes and...

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Bibliographic Details
Published in2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) pp. 480 - 483
Main Authors Rathore, Shivansh, Rana, Tanishka, Mittal, Utkarsh, Gupta, Tejasvi, Malik, Nahid, A S, Mohammed Shariff
Format Conference Proceeding
LanguageEnglish
Published IEEE 12.05.2023
Subjects
Artificial neural networks
Brain modeling
Brain Tumor
CNN
Computational modeling
Computer architecture
Deep Learning
Image processing
Magnetic resonance imaging
MRI
ResNet-50
Transfer learning
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Summary:Tumor is an uncontrollable growth of brain and its surrounding tissues. The probably rate of survival of a person having a tumor depends on various factors such as location, kind, size of tumor and age of patient and overall health. Detection of brain tumors is key to improving patient outcomes and increasing the chances of treatment success. Magnetic resonance imaging (MRI) is a well-known used imaging technique to detect brain tumor. The study aims to compare ResNet-50's performance on publicly available datasets of brain MRI images to that of other deep neural networks. Images of normal brain tissue and brain tumours are included in the dataset. Using the dataset, the researchers trained and tested the networks, evaluating their performance in terms of F1-score, accuracy, precision and overall efficiency.
DOI:10.1109/ICACITE57410.2023.10183081