Brain Tumor Detection Using Deep Learning Approaches
Razia Sultana Misu
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.