Robust Multi-class Brain Tumor Classification Using Hybrid Transfer Learning

Background: Classification of brain tumors is an integral aspect of medical imaging studies as different tumor features require multiclassification which aids in making the diagnosis. However, this task is of great difficulty owing to the nature of the brain MRI images. Recent deep learning (DL) models have made it possible to have a brain tumor classification with high accuracy, which is very useful to neurologists. Based on these new capabilities, this study intends to enhance brain tumor detection with the help of a hybrid transfer learning approach. Methods: This study developed a brain tumor diagnosis system using five advanced DL architectures: Xception, ResNet164, DenseNet121, DenseNet201, and Inception-ResNetV2. A hybrid DL model was created by integrating a deep-dense block and a softmax activation function in the final layers of these architectures. The proposed approach enhances classification accuracy and precision. Two experiments were conducted: Three-class classification, involving images from patients with gliomas, meningiomas, and pituitary tumors. Four-class classification, which included gliomas, meningiomas, pituitary tumors, and normal brain images. Result