Brain Tumor Detection and Classification Using Transfer Learning Models

Abstract

Diagnosing brain tumors is a time-consuming process requiring radiologist expertise. With the growing patient population and increased data volume, conventional procedures have become expensive and ineffective. Scholars have explored algorithms for detecting and classifying brain tumors, focusing on precision and efficiency. Deep learning methodologies are being used to create automated systems that can diagnose or segment brain tumors with precision and efficiency, particularly in brain cancer classification. This approach facilitates transfer learning models in medical imaging. The present study undertakes an evaluation of three foundational models in the domain of computer vision, namely AlexNet, VGG16, and ResNet-50. The VGG16 and ResNet-50 models demonstrated praiseworthy performance, thereby instigating the amalgamation of these models into a groundbreaking hybrid VGG16–ResNet-50 model. The amalgamated model was subsequently implemented on the dataset, yielding a remarkable accuracy of 99.98%, sensitivity of 99.98%, and specificity of 99.98% with an F1 score of 99.98%. Based on a comparative analysis with alternative models, it can be deduced that the suggested framework exhibits a commendable level of dependability in facilitating the timely identification of diverse cerebral neoplasms.

Keywords

• brain tumor
• transfer learning
• AlexNet
• VGG16
• ResNet-50
• deep learning