Brain Tumor Classification and Severity Identification Using Deep Convolutional Spiking U-Net Lyrebird Neural Network and Alpha Piecewise Linear-Fuzzy

This work presents a novel method that integrates spiking neural networks, Lyrebird Optimization, and Alpha-Piecewise Linear-Fuzzy, resulting in an explainable and unified approach to analyzing multimodal brain tumor imaging.

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Publication Details

Journal
Cancer Biotherapy and Radiopharmaceuticals
Published
2026-04-02
DOI
https://doi.org/10.1177/10849785261431191
Primary Topic
Brain Tumor Detection and Classification
Type
article
Field-Weighted Citation Impact
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article

Brain Tumor Classification and Severity Identification Using Deep Convolutional Spiking U-Net Lyrebird Neural Network and Alpha Piecewise Linear-Fuzzy

Anumula Kalyan Kumar, M.S. Radha Manga Mani, Hemanand Chittapragada, Saggurthi Ramesh et al.
Cancer Biotherapy and Radiopharmaceuticals
Brain Tumor Detection and Classification
article

Brain Tumor Classification and Severity Identification Using Deep Convolutional Spiking U-Net Lyrebird Neural Network and Alpha Piecewise Linear-Fuzzy

Anumula Kalyan Kumar, M.S. Radha Manga Mani, Hemanand Chittapragada, Saggurthi Ramesh, Sivaramaiah Yenugu
article en

Abstract

This work presents a novel method that integrates spiking neural networks, Lyrebird Optimization, and Alpha-Piecewise Linear-Fuzzy, resulting in an explainable and unified approach to analyzing multimodal brain tumor imaging.

Cancer Biotherapy and Radiopharmaceuticals
OSRAM (United States) (US), Kanchi Mamunivar Centre for Post Graduate Studies (IN), La Maddalena (IT), Artificial Intelligence in Medicine (Canada) (CA), Koneru Lakshmaiah Education Foundation (IN)
Reduced inequalities
Openalex Percentile: Top 24%
Brain Tumor Detection and Classification
DOISourceOpenAlex
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