Automated Brain Tumor Segmentation in MRI Using AI for Improved Neurodiagnostics

Title

Automated Brain Tumor Segmentation in MRI Using AI for Improved Neurodiagnostics

Creator

Upreti, Kamal; George, Jossy; Malik, Khushboo

Description

Early and accurate classification of brain tumors plays a pivotal role in clinical decision-making and treatment planning. Manual methods are time-intensive and prone to variability, creating a need for robust automated solutions. This study aims to classify brain tumors from MRI scans using artificial intelligence techniques, specifically Logistic Regression (LR) and Support Vector Machines (SVM) with Radial Basis Function (RBF) kernels. The dataset, sourced from The Cancer Imaging Archive (TCIA), includes four classes: Meningioma, Glioma, Hypothalamic tumor, and No tumor. Preprocessing involved dimensionality reduction using Principal Component Analysis (PCA) to retain dominant features. Models were trained on an 80:20 train-test split, with LR achieving 99.83% training and 78.91% testing accuracy, while SVM performed better with 93.85% training and 81.88% testing accuracy. Error analysis revealed 104 misclassified samples, primarily due to structural similarity among tumor types. The findings suggest that SVM offers superior classification performance, and the study recommends further enhancement through deep learning models like Convolutional Neural Networks (CNNs) for improved diagnostic accuracy. 2025 Oriental Scientific Publishing Company. All rights reserved.

Source

Biomedical and Pharmacology Journal;Volume;18;Issue;2;pp.1257-1271

Date

01-01-2025

Publisher

Oriental Scientific Publishing Company

Subject

Classification; Diagnosis; Early Detection; Machine Learning; MRI; Neuro-images

Coverage

Upreti K., Department of Computer Science and Engineering, Christ University, Delhi NCR, Uttar Pradesh, Ghaziabad, India; George J., Department of Computer Science and Engineering, Christ University, Delhi NCR, Uttar Pradesh, Ghaziabad, India; Malik K., Department of Law, Christ University, Delhi NCR, Uttar Pradesh, Ghaziabad, India

Rights

All Open Access; Gold Open Access

Relation

ISSN: 9746242;

Format

online

Language

English

Type

Article

Identifier

https://doi.org/10.13005/bpj/3167

https://www.scopus.com/pages/publications/105011041787?origin=resultslist