Brain Tumor Detection by using Fine-tuned MobileNetV2 Deep Learning Model

Most of the deaths in world happen due to Cancer. It is a disease which cells our body organs or tissues grow an undisciplined way turn can harm normal and body. These very smartly trick immune system so that cancerous are kept alive not destroyed. In human body, tumors be classified into three types: cancerous, non-cancerous, pre-cancerous. Timely identification cancer helpful many ways. As it improves patient’s chances survival. The most valuable, uncomplicated technique used MRI scans for predicting tumor tough task have error. So more accurate with predictions we moved on use computerized techniques ease work. focus this research development automated brain classification using magnetic resonance imaging (MRI) scans, leveraging deep learning model. proposed model employs convolutional neural network (CNN) architecture known as MobileNetV2, trained pre-processed image dataset classify one two categories: tissue. To mitigate overfitting expand dataset, data augmentation employed. achieves high accuracy, sensitivity, specificity classifying tumors. Proposed CNN outperformed other models, including VGG16, Xception, ResNet50, were comparison.