INCS: A Novel Brain Tumor Detection Methodology Design Based on Deep Learning Assisted Intelligent Neural Classification Strategy

Abstract

Brain tumors must be detected as soon as possible. Biopsies can only be taken after final brain surgery, which is the only way to classify brain tumors. Brain tumors can be better identified and classified with the use of computational intelligence-oriented approaches. In order to help doctors detect tumors early on with high accuracy, we presented two deep learning methods and multiple machine learning approaches for diagnosing three different types of brain tumors using magnetic resonance brain images: glioma, meningioma, and pituitary gland tumors. We also included healthy brains without tumors. By training on any dataset, deep learning algorithms may extract features. These methods are built on multi-layered neurons. One kind of deep learning technique is the Convolutional Neural Network (CNN), which is designed to extract features from datasets with rich spatial information, like images. Using that reasoning as a foundation, this work introduces a novel model, the Intelligent Neural Classification Strategy (INCS), and tests its efficacy through cross-validation with the more traditional CNN model. When brain tissue grows abnormally and becomes malignant when left untreated, it is known as a brain tumor. An MRI scan is the first step in the diagnostic process, followed by a trained radiologist's visual examination. Computerizing the work of brain MRI categorization is an actively explored issue since it is tough to find trained professionals in poor countries. A customized INCS has been taught to detect tumors on MRI images in this research study. Researchers check the model's correctness by comparing it to other models they've developed.

Topics & Concepts