A CNN Approach to Automated Detection and Classification of Brain Tumors
Journal:
arXiv
Published Date:
Feb 13, 2025
Abstract
Brain tumors require an assessment to ensure timely diagnosis and effective
patient treatment. Morphological factors such as size, location, texture, and
variable appearance complicate tumor inspection. Medical imaging presents
challenges, including noise and incomplete images. This research article
presents a methodology for processing Magnetic Resonance Imaging (MRI) data,
encompassing techniques for image classification and denoising. The effective
use of MRI images allows medical professionals to detect brain disorders,
including tumors. This research aims to categorize healthy brain tissue and
brain tumors by analyzing the provided MRI data. Unlike alternative methods
like Computed Tomography (CT), MRI technology offers a more detailed
representation of internal anatomical components, making it a suitable option
for studying data related to brain tumors. The MRI picture is first subjected
to a denoising technique utilizing an Anisotropic diffusion filter. The dataset
utilized for the models creation is a publicly accessible and validated Brain
Tumour Classification (MRI) database, comprising 3,264 brain MRI scans. SMOTE
was employed for data augmentation and dataset balancing. Convolutional Neural
Networks(CNN) such as ResNet152V2, VGG, ViT, and EfficientNet were employed for
the classification procedure. EfficientNet attained an accuracy of 98%, the
highest recorded.
