Brain tumor is a progressive disease that significantly affects human life. These diseases can reduce the quality of life by causing losses in motor and cognitive functions. Since the chance of treatment decreases when not diagnosed early, advanced technologies are needed for diagnosis and prognosis. In this study, the applicability of transfer learning-based approaches for the classification of brain tumors was investigated. For this purpose, the classification performance of a proposed convolutional neural network model was compared with the performances of pre-trained transfer learning models such as VGG16, ResNet50 and InceptionV3. The designed models were trained on two different brain tumor datasets with two and four classes. The obtained results were analyzed by comparing the performance parameters of each model on different datasets. As a result, pre-trained transfer learning models supported by fine-tuning applications showed high performance in both two-class and four-class brain tumor classification tasks.

Keywords - Brain tumor classification, deep learning, transfer learning, fine tuning

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