Abstract:
critical to improving patient outcomes. However, manual classification of brain tumors
remains challenging due to the complex nature of tumor characteristics and the time
intensive nature of the process. This study seeks to address these challenges by creating
advanced machine learning models, including Convolutional Neural Networks (CNNs),
Support Vector Machines (SVMs), and hybrid models, to classify brain tumors into four
categories: pituitary, meningioma, glioma, and non-tumor. Utilizing a dataset of brain
MRI images, preprocessing techniques such as image normalization, resizing, and
augmentation were applied to enhance model robustness and generalization. The dataset
was divided into training and testing sets in proportions of 87% and 13%, respectively,
ensuring a comprehensive evaluation of model performance. Leveraging the LeVit
model as a combination of ViT and CNN elements, and involving data augmentation
and optimal hyperparameter tuning with the Optuna module, the system was developed
to optimize classification accuracy. The proposed automated system achieved a test
accuracy of 99%, demonstrating its potential for reliable and efficient brain tumor
detection. By improving the accuracy and speed of brain tumor diagnosis, this system
contributes to more effective patient management and timely treatment interventions,
ultimately aiding in the reduction of mortality rates associated with brain tumors. The
automated classification system presents significant advancements by contributing to
the medical field.
