ItemArticle

Pediatric brain tumor segmentation and classification framework using SGC-U-NET and ARC-DEEP-CNN

Title
Pediatric brain tumor segmentation and classification framework using SGC-U-NET and ARC-DEEP-CNN
Creator
Praghash, K.; Chalasani, Vidyadhari; Balan, Santhosh Kumar; Daniya, T.
Description
Timely and precise pediatric Brain Tumor (BT) classification is challenging in the prevailing studies owing to the lack of growth rate calculation. Therefore, this paper proposes a growth rate-aware intelligent BT classification using child Magnetic Resonance Imaging (MRI) based on Arcsin Deep Convolutional Neural Network (Arc-Deep-CNN). Initially, the child's MRI is collected and then pre-processed for angle correction, resolution improvement, skull removal, and edge sharpening to improve the image quality. Meanwhile, the binary image dilation is done in the postpre-processing for accurate tumor location identification using the Central Limit Theorem-based Battle Royale Optimization Algorithm (CLT-BROA). From the pre-processed images, the wavelet features are extracted to improve the detection rate. Based on the tumor-identified images, pre-processed images, and extracted features, a robust Shuffled Group Convolutional layer added U-Net (SGC-U-Net) significantly segments the normal brain, benign, core, and malignant tumors affected brain. Then, the 3D tumor reconstruction is done by performing splitting, feature extraction, and growth rate calculation. Finally, a novel Arc-Deep-CNN proficiently classifies the BT into Medulloblastoma, Glioma, and Meningioma tumors with respect to the growth rate. The proposed Arc-Deep-CNN achieved maximum accuracy and minimum training time of 98.77% and 52136ms, respectively, showing impressive performance in pediatric BT classification. Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.
Source
International Journal of Information Technology (Singapore);
Date
01-01-2025
Publisher
Springer Science and Business Media B.V.
Subject
Arcsin deep convolutional neural network (Arc-Deep-CNN); Brain tumor; Central limit theorem-based battle royale optimization algorithm (CLT-BROA); Magnetic resonance imaging (MRI); Medulloblastoma; Shuffled group convolutional layer added U-net (SGC-U-Net)
Coverage
Praghash K., Department of Electronics and Communication Engineering, Christ University, Bengaluru, India; Chalasani V., Department of Information Technology, Gokaraju Rangaraju Institute of Engineering and Technology, Bachupally, Hyderabad, India; Balan S.K., Department of Computer Science and Engineering, Guru Nanak Institute of Technology, Ranga Reddy (District), Ibrahimpatnam, Telangana, 501506, India; Daniya T., Department of CSE (AI & ML), GMR Institute of Technology, Rajam, Andhra Pradesh, Vizianagaram, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 25112104;
Format
online
Language
English
Type
Article

Citation

Praghash, K.; Chalasani, Vidyadhari; Balan, Santhosh Kumar; Daniya, T., “Pediatric brain tumor segmentation and classification framework using SGC-U-NET and ARC-DEEP-CNN,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 20, 2026, https://archives.christuniversity.in/items/show/22105.

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