BEYİN TÜMÖRÜNÜN VERİMLİ SINIFLANDIRILMASI İÇİN MAKİNE ÖĞRENME YÖNTEMLERİ ARAŞTIRMASI

Abstract

The review, analysis, and comparison of the performance of pre-trained models for the classification of brain tumors based on magnetic resonance imaging is the primary objective of this thesis (MRI). In the first part of the study, some background information on artificial neural networks, such as perceptrons and multilayer perceptrons, as well as the frameworks that are going to be used in the study, is presented. The background of brain tumors, including their causes, symptoms, and diagnostic approaches, is also covered in this article. The use of pre-trained models in image classification, and more specifically in brain tumor MRI classification, is analyzed in the section of the paper devoted to the literature review. Additionally, the various classification algorithms that are put to use in this area of research are discussed. The MRI brain tumor dataset and the pre-trained models (VGG16, ResNet50, and InceptionV3) that will be compared are then described in detail in the following section of the study. After that, feature extraction, fine tuning, transfer learning, and a combination of these four methods are put into action and evaluated as potential methods for classifying brain tumors using the previously trained models. The outcomes of these methods are charted and compared using a variety of metrics, and the models that perform the best are retained for further examination. The purpose of this study is to provide insight into the most effective methods for classifying brain tumors using pre-trained models and to suggest potential directions for future research in this area. The models used in this study were obtained from the Brain Tumor Classification Dataset available publicly on Kaggle.