000			02580nam a22002657a 4500
003			KOHA
005			20241007144235.0
008			240925d2024 cy dj||| |||| 00| 0 eng d
040			_aCY-NiCIU _beng _cCY-NiCIU _erda
041			_aeng
090			_aYL 3427 _bH36 2024
100	1		_aHamed, Kamila Abdulhamıd Saleh
245	1	0	_aBRAIN TUMOR CLASSIFICATION USING DEEP LEARNING: A COMPARATIVE ANALYSIS OF VGG19, EFFICIENTNETB3, AND RESNET50 PERFORMANCE / _cKAMILA ABDULHAMID SALEH HAMED ; SUPERVISOR, ASSOC. PROF. DR. UMAR ÖZGÜNALP
264			_c2024
300			_a51 sheets; _c30 cm _e+1 CD ROM
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
502			_aThesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Electrical and Electronic Engineering
520			_aIn 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.
650		0	_aElectrical and Electronic Engineering _vDissertations, Academic
700	1		_aÖzgünalp, Umar _esupervisor
942			_2ddc _cTS
999			_c292854 _d292854