Brain Tumor Classification using VGG 16, ResNet50, and Inception V3 Transfer Learning Models

Abstract

Brain Tumor is a deadly diagnosis that, if not detected early on, can cause significant damage to a patient’s brain, disrupting the body’s functions and even leading to death. However, the conventional method of detecting brain tumors is conducting MRI scans which a medical expert then consults for diagnosis. While time-consuming, this method also leaves room for human error, especially in cases where the tumor is in its early stages. Thus, the diagnosis of brain tumors must be made accurately in the least time possible. This paper aims to prevent premature mortality, provide health in resource-constrained settings, and seek patients’ healthy lives, which can be done through timely and accurate diagnosis of brain tumors. In this paper, three different deep transfer learning models are used after adding fine-tuned layers to detect brain tumors in a dataset of 251 Magnetic Resonance Imaging (MRI) scans. The transfer learning models used are VGG16, InceptionV3, and ResNet50, with fine-tuned Dropout, Flatten, and Dense added layers. The highest accuracy was achieved with the VGG16 model, which had an accuracy of 91.58%. Thus, deep learning models are proven effective in detecting brain tumors without the unwanted expense of time and resources.