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A Deep Multiscale Neural Network for Accurate Neurological Disorder Detection from MRI Scans and Real-Time Web Deployment

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Neurological disorders involve diverse pathologies of the brain and nervous system, making early and accurate detection essential. While many deep CNNs have been developed for MRI-based classification of neurological disorders, most are optimized for binary tasks and often fail to capture the multi-class features needed to distinguish subtle anatomical differences across conditions.

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arXiv Machine Learning
Sep 16

NeuroTS-Net: Multi-Class Semantic Segmentation of Pediatric Brain Tumors in Multi-Modal MRI

NeuroTS-Net is a 3‑D encoder‑decoder CNN designed for multi‑class semantic segmentation of pediatric brain tumors in multi‑modal MRI. It uses a dual‑scale raw‑detail stream, adaptive low‑resolution context selection, and detail‑preserving multipath downsampling to maintain fine intensity and boundary information while modeling broader tumor context. Trained on the BraTS 2026 pediatric dataset, it outperformed nnU‑Net and MedNeXt, achieving Dice scores of 0.938/0.937 on internal validation and 0.927/0.926 on the official challenge set.

By Darius Peteleaza, Razvan-Gabriel Dumitru, Bogdan Neamtu, Arpad Gellert, Mariana Sandu, Claudiu Matei