Automated Brain Tumor Classification and Grading Using Multi-scale Graph Neural Network with Spatio-Temporal Transformer Attention Through MRI Scans.
Authors
Affiliations (5)
Affiliations (5)
- Department of Computer Science, ABES Engineering College, Ghaziabad, 201009, India.
- Department of CSE, KIET Group of Institutions, Ghaziabad, 201206, India.
- United College of Engineering and Research, Prayagraj, 211010, India.
- Department of Computer Science, KIET Group of Institutions, Ghaziabad, 201206, India.
- Department of Information Technology, Manipal University Jaipur, Jaipur, 303007, India. [email protected].
Abstract
The medical field uses Magnetic Resonance Imaging (MRI) as an essential diagnostic tool which provides doctors non-invasive images of brain structures and pathological conditions. Brain tumor detection stands as a vital application that needs specific and effective approaches for both medical diagnosis and treatment procedures. The challenges from manual examination of MRI scans stem from inconsistent tumor features including heterogeneity and irregular dimensions which results in inaccurate assessments of tumor size. To address these challenges, this paper proposes an Automated Classification and Grading Diagnosis Model (ACGDM) using MRI images. Unlike conventional methods, ACGDM introduces a Multi-Scale Graph Neural Network (MSGNN), which dynamically captures hierarchical and multi-scale dependencies in MRI data, enabling more accurate feature representation and contextual analysis. Additionally, the Spatio-Temporal Transformer Attention Mechanism (STTAM) effectively models both spatial MRI patterns and temporal evolution by incorporating cross-frame dependencies, enhancing the model's sensitivity to subtle disease progression. By analyzing multi-modal MRI sequences, ACGDM dynamically adjusts its focus across spatial and temporal dimensions, enabling precise identification of salient features. Simulations are conducted using Python and standard libraries to evaluate the model on the BRATS 2018, 2019, 2020 datasets and the Br235H dataset, encompassing diverse MRI scans with expert annotations. Extensive experimentation demonstrates 99.8% accuracy in detecting various tumor types, showcasing its potential to revolutionize diagnostic practices and improve patient outcomes.