A hybrid transfer learning model for brain tumor classification

Brain tumors are one of the most common and fatal diseases globally. It is crucial to detect brain tumors at an early stage to increase the patient's life expectancy. Nonetheless, classifying brain tumors manually is a difficult and time-consuming task. A hybrid transfer learning model is suggested in this study to classify brain tumors automatically. The four steps of the suggested approach are preprocessing and data augmentation, fusion of deep feature extractions, fine-tuning, and classification. VGG16, ResNet50, and MobileNetV2 CNN pre-trained models are fused to increase the number of informative features and reduce overfitting. The suggested model is validated on four public available datasets: Br35H, Nickparvar, Figshare, and Sartaj. The proposed model achieved the highest accuracy values in all datasets: 99.66% on Br35H, 97.56% on Figshare, 97.08% on Nickparvar, and 93.74% on Sartaj. The suggested model is more effective than other cutting-edge models in classifying brain tumors.