BRAIN TUMOR CLASSIFICATION USING DEEP LEARNING: A COMPARATIVE ANALYSIS OF VGG19, EFFICIENTNETB3, AND RESNET50 PERFORMANCE /
KAMILA ABDULHAMID SALEH HAMED ; SUPERVISOR, ASSOC. PROF. DR. UMAR ÖZGÜNALP
- 51 sheets; 30 cm +1 CD ROM
Thesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Electrical and Electronic Engineering
In our current times, with the alarming spread of brain tumors of various types and causes, the development and improvement of deep models to expedite and simplify the process of detecting and classifying brain tumors is of utmost importance. Therefore, in this study, we conduct a comparative analysis of three pretrained deep learning models VGG19, EfficientNetB3, and ResNet50 with the BTMRI dataset from Kaggle. The models were implemented and trained using the Google Colab environment. The evaluation of the models is based on their validation accuracy in classifying brain tumor images. Initially, the models were trained on this BTMRI dataset to evaluate their performances which demonstrated varying levels with validation accuracies of 93.89% for VGG19, 99.09% for EfficientNetB3, and 98% for ResNet50. Then the performance of these models was enhanced using the Optuna hyperparameters optimization library to tune three main hyperparameters learning rate, dropout rate, and number of epochs, due to this the models achieved higher validation accuracy rates of 95.42% for VGG19, 99.73% for EfficientNetB3, and 99.54% for ResNet50. Overall This study highlights the effectiveness of deep learning models in medical image classification and the impact of hyperparameter optimization in improving model performance. These findings could guide future research and practical applications in the field of medical diagnostics, contributing to the detection of brain tumors more accurately and efficiently.
Electrical and Electronic Engineering--Dissertations, Academic