Machine Learning based Brain Tumor Detection using Transfer Learning

This paper investigates the use of transfer learning in MRI-based brain tumor detection. The goal is to develop a reliable and efficient model for accurately classifying brain tumors, addressing the challenging task of interpreting complex and variable tumor characteristics. By leveraging transfer learning, the model can benefit from prior knowledge and improve accuracy. The study emphasizes the significance of early diagnosis and treatment planning in enhancing patient outcomes. In this research, the proposed model is trained on a large dataset of MRI images and fine-tuned using transfer learning to improve its accuracy and efficiency. The performance of the model is evaluated using precision, recall, F1-score, and support metrics, and an average accuracy of 97% is achieved. The proposed model can assist medical professionals in making accurate diagnoses and improve patient outcomes.