OICC Press Signal Processing and Renewable Energy (SPRE) 2588-7335 5 3 2021 09 01 Brain Tumor Detection Using Deep Transfer Learning Method 41 49 EN Alireza Fazelnia Department of Biomedical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran Hassan Masoumi Department of Biomedical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran Mohammad Fatehi Department of Electrical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran Jasem Jamali Department of Electrical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran Journal Article 2021 09 01 Accurate brain tumor MR images detection plays an important role in diagnosis and treatment decision making. The machine learning methods for classification only uses low-level or high-level features, to tackle the problem of classifications using some handcrafted features. Development on deep learning, transfer learning and deep convolution neural networks (CNNs) has shown great progress and has succeeded in the image classification task. Deep learning is very powerful for feature representation. In this study, deep transfer learning method for features extraction and detection is used that it does not use any handcrafted features, and needs minimal preprocessing. Transfer learning is a method of transferring information during training and testing. In this study, features extraction from images with pre-trained CNN method, namely, GoogLeNet, VGGNet and AlexNet, for tumor detection is used. The accuracy of tumor detection is 99.84%. The results show that our method, shows the best accuracy for detections tumor