Authors :
Eliackim MUHOZA; Dr. Musoni Wilson
Volume/Issue :
Volume 8 - 2023, Issue 2 - February
Scribd :
https://bit.ly/3mFF8Cm
DOI :
https://doi.org/10.5281/zenodo.7716372
Abstract :
- In today's world, manually examining a large
number of MRI (magnetic resonance imaging)images
and detecting a brain tumor is a time-consuming and
incorrect task. It may have an impact on the patient's
medical therapy. It might be a time-consuming task
because to the large amount of image data sets involved.
Because normal tissue and brain tumor cells have a lot in
common in terms of appearance, segmenting tumor
regions can be difficult. As a result, a highly accurate
automatic tumor detection approach is required. In this
study, I useda convolutional neural network to segregate
brain tumors from 2D magnetic resonance brain images
(MRI) and then compared the results. Moreover, I
conducted the research on the six traditional classifiers
namely- Support Vector Machine (SVM), K-Nearest
Neighbor(KNN), Multi-layer Perceptron (MLP), Logistic
Regression, Naive Bayes and Random Forest and deep
learning approaches then compared with a convolutional
neural network(CNN).To properly train this algorithm, I
took a variety of MRI pictures with a variety of tumor
sizes, locations, forms, and image intensities. We used
«TensorFlow" and" Keras "in" Python" to develop the
solution because it is an efficient programming language
for performing rapid work. I also performed a literature
review on this topic, and the study concluded with a
recommendation for additional researchin this area.
- In today's world, manually examining a large
number of MRI (magnetic resonance imaging)images
and detecting a brain tumor is a time-consuming and
incorrect task. It may have an impact on the patient's
medical therapy. It might be a time-consuming task
because to the large amount of image data sets involved.
Because normal tissue and brain tumor cells have a lot in
common in terms of appearance, segmenting tumor
regions can be difficult. As a result, a highly accurate
automatic tumor detection approach is required. In this
study, I useda convolutional neural network to segregate
brain tumors from 2D magnetic resonance brain images
(MRI) and then compared the results. Moreover, I
conducted the research on the six traditional classifiers
namely- Support Vector Machine (SVM), K-Nearest
Neighbor(KNN), Multi-layer Perceptron (MLP), Logistic
Regression, Naive Bayes and Random Forest and deep
learning approaches then compared with a convolutional
neural network(CNN).To properly train this algorithm, I
took a variety of MRI pictures with a variety of tumor
sizes, locations, forms, and image intensities. We used
«TensorFlow" and" Keras "in" Python" to develop the
solution because it is an efficient programming language
for performing rapid work. I also performed a literature
review on this topic, and the study concluded with a
recommendation for additional researchin this area.
