Detection of Brain Tumor using VGG16

Abstract

The detection of brain tumors plays a crucial role in medical imaging, and machine learning techniques have shown great potential in improving the accuracy and efficiency of this process. In recent years, deep convolutional neural networks (CNNs) such as VGG-16 have been successfully applied to this task, achieving high levels of accuracy in tumor detection. The VGG-16 model is a deep CNN architecture that has been trained on a large dataset of images, allowing it to learn complex features that are useful for classifying brain tumor images. By leveraging the power of transfer learning, the model can be fine-tuned on a smaller dataset of brain tumor images, allowing it to learn specific features that are relevant to this task. Here, we offer a method for leveraging the VGG-16 model to find brain cancers. We first pre- process the images to enhance the contrast and remove noise, then extract features from the images using the VGG-16 model. After that, these features are applied to build a SVM classifier to distinguish between images with and without tumors. The proposed results show that the VGG-16 model is highly effective in detecting brain tumors, achieving an accuracy of over 95%. This approach has the potential to significantly improve the efficiency and accuracy of brain tumor detection, allowing doctors to diagnose and treat patients more quickly and effectively.