A Reliable Method for Detecting Brain Tumors in Magnetic Resonance Images Utilizing EfficientNe

A brain tumor occurs when there is an atypical
proliferation of cells in the brain, resulting in abnormal growth.
The survival rate of patients with brain tumors is difficult to
determine due to their infrequent occurrence and various forms.
Magnetic Resonance Imaging (MRI) plays a crucial role in
identifying tumor sites, but manual detection is time-consuming
and prone to errors. Innovative breakthroughs in artificial
intelligence, particularly in the realm of deep learning (DL), have
paved the way for the creation of DL models that utilize MRI
images for diagnosing brain tumors. In this paper, a three-step
preprocessing approach is proposed to enhance the quality of
MRI images, along with a Convolutional Neural Network (CNN)
based on the EfficientNet-B0 model for accurate diagnosis of
glioma, meningioma, pituitary tumors, and normal images. The
model is designed to be computationally efficient, featuring a
small number of convolutional and max-pooling layers, which
allows for swift training iterations. The model achieved a 95.81%
accuracy in detecting glioma, 97.54% accuracy in detecting
meningioma, 96.89% accuracy in detecting pituitary tumors, and
97.14% accuracy in detecting normal images when tested on a
dataset of 3394 MRI images.