Detection and Classification of Brain Tumor using Fine Tuned Mobile Net Algorithm

Using medical image analysis, this work investigates brain tumor detection and classification. Based on their source, brain tumors, an uncontrolled cell proliferation within the brain are considered to fall into two main types. One is principal; the other is secondary. Magnetic resonance imaging (MRI) examinations are the most effective and beneficial technique. The task of predicting the type of malignancy by examining the images is challenging, and if performed manually, there is a risk of human error.The utilization of these methods for the detection of brain tumors has been made possible by recent technological advancements. We suggest that a MobileNet base model be fine-tuned with supplementary information in this investigation. By restructuring its layers, the model’s precision and accuracy were improved. Apart from the recommended approach, which produced similar results, the paper explores the usage of the MobileNet algorithm for brain tumor classification. Overall, the combination of deep learning techniques with good designs seems to be able to identify and classify brain tumors with dependability