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Open Access

ARTICLE

LiteDKT-Net: A Lightweight Diverse Kernel Transformer Network for Brain Tumor Segmentation

Ronak Patel1, Miral Patel2, Deep Kothadiya3, Bayan AlGhofaily4, Faten S. Alamri5,*, Awad Alyousef4, Amjad R Khan4
1 U & P U Patel Department of Computer Engineering, Chandubhai S. Patel Institute of Technology (CSPIT), Faculty of Technology (FTE), Charotar University of Science and Technology (CHARUSAT), Changa, Anand, India
2 G H Patel College of Engineering and Technology, CVM University, V V Nagar, Anand, India
3 Symbiosis Center for Information Technology, Symbiosis International (Deemed University), Pune, India
4 AIDA Lab. CCIS, Prince Sultan University, Riyadh, Saudi Arabia
5 Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
* Corresponding Author: Faten S. Alamri. Email: email
(This article belongs to the Special Issue: Novel Methods for Image Classification, Object Detection, and Segmentation, 2nd Edition)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.085703

Received 16 May 2026; Accepted 13 July 2026; Published online 18 August 2026

Abstract

Growth of cancerous cells is unpredictable, and their effects vary across organs and levels of aggression. Identification of the pattern, size, and shape of the growth helps assess severity for better treatment. The proposed LiteDKT-Net combines the DK-IRB (Diverse Kernel Inverted Residual Block) block and Transformer to target conceptual information about shape and location. For better edge detection, LiteDKT-Net uses GAG (Group Attention Gate) followed by CBAM (Convolutional Block Attention Module). LiteDKT-Net is a lightweight encoder-decoder-based network optimized for accurate brain tumor segmentation. The network parameter optimization and reduced computational complexity in LiteDKT-Net enable high segmentation accuracy while maintaining a lightweight model size for deployment in clinical environments with limited resources. The proposed architecture achieves Dice score similarities of 0.80, 0.82, and 0.87 for ET (Enhancing Tumor), TC (Tumor Core), and Whole Tumor (WT), respectively. To check the difference between the predicted and ground truth with HD95, the results are 5.24, 8.12, and 8.01 mm for ET, TC, and WT. The proposed architecture evaluates 0.869M parameters and uses 0.856 Giga Floating-Point Operations Per Second (GFLOPs) of computation. Ablation analysis is carried out based on the channel-wise and kernel-wise parameters, which helps to understand model efficiency and lightweight computational cost.

Keywords

UNET; transformer; group attention gate; CBAM; 3D MRI segmentation; FLOPs

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