Brain Tumor Detection Using Deep Learning Approaches

TL;DR

This paper explores the use of deep learning models, especially ResNet50, for accurate brain tumor detection in MRI images, demonstrating high accuracy and potential for automation in medical diagnosis.

Contribution

It evaluates five transfer learning models for brain tumor detection, highlighting ResNet50's superior performance with 99.54% accuracy.

Findings

ResNet50 achieved 99.54% accuracy in tumor detection.

Deep learning models significantly improve MRI-based diagnosis.

ResNet50 outperformed other transfer learning models.

Abstract

Brain tumors are collections of abnormal cells that can develop into masses or clusters. Because they have the potential to infiltrate other tissues, they pose a risk to the patient. The main imaging technique used, MRI, may be able to identify a brain tumor with accuracy. The fast development of Deep Learning methods for use in computer vision applications has been facilitated by a vast amount of training data and improvements in model construction that offer better approximations in a supervised setting. The need for these approaches has been the main driver of this expansion. Deep learning methods have shown promise in improving the precision of brain tumor detection and classification using magnetic resonance imaging (MRI). The study on the use of deep learning techniques, especially ResNet50, for brain tumor identification is presented in this abstract. As a result, this study investigates the possibility of automating the detection procedure using deep learning techniques. In this study, I utilized five transfer learning models which are VGG16, VGG19, DenseNet121, ResNet50 and YOLO V4 where ResNet50 provide the best or highest accuracy 99.54%. The goal of the study is to guide researchers and medical professionals toward powerful brain tumor detecting systems by employing deep learning approaches by way of this evaluation and analysis.