AI-Enhanced Brain Tumor Classification using Deep Learning

DOI

10.2991/978-94-6463-754-0_19How to use a DOI?

Keywords

Glioma; Spatiotemporal; Excrescence; Growth models; DTI (Diffusion Tensor Imaging); MRI (Magnetic Resonance Imaging); Average precision (AP); Performance indicator; Low-Grade Glioma (LGG); Prognostication; Isotropic prolixity model

Abstract

Glioma spatiotemporal geste can be simulated and read using excrescence growth models in specific cases. Since glioma cell infiltration is known to occur more briefly along white matter pathways, prolixity tensor imaging (DTI) and structural glamorous resonance imaging (MRI) can be used to improve the model. However, there are still several difficulties in applying genuine case data to the operation and assessment of growth models. In this study, we suggest using average perfection (AP) as a performance indicator and frame the excrescence growth problem as a rating difficulty rather than segmentation challenge. Without using a volume cut-off criterion, this method makes it easier to evaluate spatial patterns. We assess prolixity models of proliferation guided using DTI and structural MRI following excrescence excision using the AP metric. To predict the form of intermittent excrescences after surgery, We submitted an application these models for a distinct longitudinal dataset comprising 14 individuals with Low-Grade Glioma (LGG) that had surgical resection without posterior therapy. We used these models to predict the form of intermittent excrescences after surgery in a individual longitudinal dataset that included 14 cases with low-grade glioma (LGG) that had surgical resection without posterior therapy. Our findings indicate that a notable enhancement in prognosticating the shape of intermittent excrescences can be achieved by using a DTI-informed isotropic prolixity model over isotropic prolixity, and that the AP standard is applicable for assessing these models. All law and datasets employed in this study are intimately accessible.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - G. Jawagar Sri
AU  - C. A. R. Naveen Bharathi
AU  - Rama Lakshmi
PY  - 2025
DA  - 2025/06/30
TI  - AI-Enhanced Brain Tumor Classification using Deep Learning
BT  - Proceedings of the 2025 International Conference on Advanced Research in Electronics and Communication Systems (ICARECS-2025)
PB  - Atlantis Press
SP  - 203
EP  - 213
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-754-0_19
DO  - 10.2991/978-94-6463-754-0_19
ID  - Sri2025
ER  -