Brain tumors are a critical and challenging health issue worldwide, and they often reduce patient survival if they are not identified at an early stage. This study presents an automatic approach to identify and classify brain tumor magnetic resonance imaging (MRI) scans using several pre-trained deep learning frameworks and compares their relative performance. Four popular frameworks, namely VGG16, Xception, ResNet50, and MobileNetV1, were fine-tuned with a transfer learning technique on 2870 brain tumor MRI training images to predict four classes (glioma, meningioma, pituitary tumor, and no tumor). A quantitative comparison among all applied frameworks was performed using accuracy, precisio... More >

Around the world, brain tumors are a major cause of human mortality. Accurate brain tumor detection is essential for effective treatment and improved patient outcomes. This study introduces the progressive transfer learning method, using VGG16 and MobileNet for the brain tumor identification and classification task. The outcome demonstrated the importance of the proposed models. The final accuracy of VGG16 and MobileNet on the test data was 98% and 87%, respectively, highlighting the superiority of VGG16 over the MobileNet framework. In addition, future work will explore advanced fine-tuning strategies, regularization techniques, and other methods to further improve model performance for hel... More >