Optimizing Brain Tumor Identification with Fine- Tuned Pre-Trained CNN Models A Comparative Study of VGG16 and EfficientNetB4

Authors : Yasir Mehmood; Naeem Naseer

Volume/Issue : Volume 8 - 2023, Issue 9 - September

Google Scholar : https://tinyurl.com/2s2twxu7

Scribd : https://tinyurl.com/uvbm3zfu

DOI : https://doi.org/10.5281/zenodo.10025342

Brain tumors are pathological disorders characterized by unregulated cell proliferation inside damaged tissues, demanding early identification to prevent uncontrollable development. Because of its higher image quality, magnetic resonance imaging (MRI) is a commonly used tool for the first diagnosis of brain tumors. Deep learning, a subset of artificial intelligence, has recently been integrated, ushering in novel ways to automate medical picture recognition. Transfer learning techniques applied to MRI images, this study hopes to give a reliable and effective methodology for the early diagnosis of brain tumors. This study uses a deep learning architecture using sequential Convolutional Neural Networks (CNNs) and two pre-trained models, VGG16 and EfficientNetB4, from the ImageNet dataset to classify brain tumor pictures. Image preprocessing methods are used prior to model training to improve model performance. The experiments use the BrcH35 dataset from Kaggle, which has been preprocessed in the MASK RCNN format. The top-performing transfer learning models are evaluated using performance criteria such as accuracy, precision, and F1 score. According to the results from this work, the EfficientNetB4 model beats the other models, reaching exceptional accuracy, precision, and F1 score values of 99.66%, 99.68%, and 100%, respectively. This proposed approach extends existing research in the field and illustrates its potential for faster and more reliable brain tumor detection.

Keywords : Brain tumors; Magnetic Resonance Imaging (MRI);Transfer Learning, Convolutional Neural Networks (CNNs); VGG16, EfficientNetB4.


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