A Novel Approach to Predict Brain Cancerous Tumor Using Transfer Learning

Abstract

As the most prevalent and deadly malignancy, brain tumors have a dismal survival rate when they are at their most hazardous. Using mostly traditional medical image processing methods, segmenting and classifying brain malignant tumors is a challenging and time-consuming task. Indeed, medical research reveals that categorization performed manually with the help of a person might result in inaccurate prediction and diagnosis. This is mostly due to the fact that malignancies and normal tissues are so dissimilar and comparable. The brain, lung, liver, breast, and prostate are all studied using imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound. This research makes significant use of CT and X-ray imaging to identify brain malignant tumors. The purpose of this article is to examine the use of convolutional neural networks (CNNs) in image-based diagnosis of brain cancers. It expedites and improves the treatment's reliability. As a result of the abundance of research on this issue, the provided model focuses on increasing accuracy via the use of a transfer learning method. This experiment was conducted using Python and Google Colab. Deep features were extracted using VGG19 and MobileNetV2, two pretrained deep CNN models. The classification accuracy is used to evaluate this work's performance. This research achieved a 97 percent accuracy rate by MobileNetV2 and a 91 percent accuracy rate by the VGG19 algorithm. This allows us to find malignancies before they have a negative effect on our bodies, like paralysis.