Survey of MRI Image based Brain Tumor Detection using Transfer Learning

Abstract

Brain tumor detection is a critical area in medical imaging that directly impacts early diagnosis and treatment planning. Magnetic Resonance Imaging (MRI) is the most preferred non-invasive modality for visualizing brain structures and identifying abnormalities such as tumors. In recent years, the application of deep learning, particularly transfer learning, has revolutionized brain tumor detection by enabling automated, accurate, and faster diagnostic tools. This survey aims to explore the application of transfer learning techniques on MRI images for brain tumor detection. We analyze various pre-trained models such as VGGNet, ResNet, Inception, DenseNet, and MobileNet, which are fine-tuned on medical imaging datasets. The advantages of using transfer learning, such as reduced training time and improved performance with limited labeled data, are discussed in detail. In addition, we review public datasets (e.g., BraTS, Figshare) and preprocessing techniques essential for enhancing MRI image quality. The study also addresses the performance evaluation metrics (accuracy, sensitivity, specificity, AUC) and highlights current challenges, such as domain shift, model interpretability, and data imbalance. Finally, future directions in improving model generalization and integration into clinical workflows are discussed.