S. K. Rajeev; M. Pallikonda Rajasekaran; R. Kottaimalai; T. Arunprasath; Nisha A.V.; Abdul Khader Jilani Saudagar

A Novel Hybrid Model for Brain Tumor Analysis Using Dual Attention AtroDense U-Net and Auction Optimized LSTM Network

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Author(s)

S. K. Rajeev ^1,2,* M. Pallikonda Rajasekaran ³ R. Kottaimalai ³ T. Arunprasath ³ Nisha A.V. ² Abdul Khader Jilani Saudagar ⁴

1. Department of Electronics & Communication Engineering, Kalasalingam Academy of Research and Education, TamilNadu, India

2. Department of Electronics and Communication Engineering, Younus College of Engineering and Technology, Kollam, Kerala, India

3. School of Electronics, Electrical and Biomedical Technology, Kalasalingam Academy of Research and Education, TamilNadu, India

4. Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University(IMSIU), Riyadh, Kingdom of Saudi Arabia

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2026.01.04

Received: 7 Jul. 2025 / Revised: 24 Aug. 2025 / Accepted: 7 Oct. 2025 / Published: 8 Feb. 2026

Index Terms

Brain Tumor, Dual Attention U-Net Model, Gray Level Co-Occurrence Matrix (GLCM), Cat and Mouse Optimization, Long Short Term Memory

Abstract

Timely identification of brain tumors helps improve treatment outcomes and reduces mortality. Accurate and non-invasive diagnostic tools for segmenting and classifying tumor regions in brain MRI scans are crucial for minimizing the need for surgical biopsies. This study builds a deep learning model for tumor segmentation and classification, aiming high accuracy and efficiency. A gaussian bilateral filter is used for noise reduction and to improve MRI image quality. Tumor segmentation is performed using an advanced U-Net model, the Dual Attention AtroDense U-Net (DA-AtroDense U-Net), which integrates dense connections, atrous convolution and attention mechanisms to preserve spatial detail and improve boundary localization. Texture-based radiomic features are subsequently extracted from the segmented tumor
region using Kirsch Edge Detector (KED) and Gray-Level Co-occurrence Matrix (GLCM) and refined through feature selection to reduce redundancy using the Cat-and-Mouse Optimization (CMO) algorithm. Tumor classification employs an Auction-Optimized hybrid LSTM Network (AOHLN). Evaluated on BraTS 2019 and 2020 datasets, the developed model achieved a Dice Similarity Coefficient of 0.9907 and a Jaccard Index of 0.9816 for segmentation accuracy and an overall accuracy of 98.99% for classification, highlighting its potential as a dependable and non-invasive diagnostic solution.

Cite This Paper

S. K. Rajeev, M. Pallikonda Rajasekaran, R. Kottaimalai, T. Arunprasath, Nisha A. V., Abdul Khader Jilani Saudagar, "A Novel Hybrid Model for Brain Tumor Analysis Using Dual Attention AtroDense U-Net and Auction Optimized LSTM Network", International Journal of Intelligent Systems and Applications(IJISA), Vol.18, No.1, pp.45-61, 2026. DOI:10.5815/ijisa.2026.01.04

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