Abstract:
The brain is the main nervous system in the human body. The brain provides all the instructions
to the whole human body. The brain can be affected by a tumor or irregular tissues when our
brain is affected by a tumor we called it a brain tumor. The brain tumor is mainly of two types
first one is the Primary Tumor and the second one is the Secondary Tumor. This tumor can be
a cause of human death. Brain tumors have mainly three different classes first one is
Meningioma tumor second one is the Glioma tumor and the last one is the Pituitary tumor.
Among these several classes of tumor Gliomas are the most dangerous and life-threatening
brain tumor with exceptionally quick development. Gliomas detection using CAD (ComputerAided Detection) is a very challenging task. Because of different shapes, sizes, and boundaries
of tumors with the surrounding area. Magnetic Resonance Imaging (MRI) is the most widely
used method for imaging structures in the human brain. In this study, a deep learning-based
method is used which has different modalities for the detection of brain tumors. The proposed
YOLOv5 deep learning method is used for detecting brain tumors. YOLO stands for You Only
Look Once. YOLOv5 model is mainly a branch of CNN (Convolution Neural Network) with
32 layers. This model is used because it has the power to detect small things. The proposed
method is validated on the BRATS dataset. We have used this data with the process of data
preprocessing, normalization, and data labeling with different classes of brain tumors. We used
80% of the data for training and 20% of the data for validation. After training the YOLOv5
model we have tested 20% of different data and got an accuracy of 91%.
