Author

Proloy Kumar Mondal

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Open access Nov 2026

DUET-Net: A Deformable CNN–Transformer with Calibrated Dual Uncertainty for Brain Tumor MRI Classification

Background: Magnetic resonance imaging (MRI) is a primary modality for brain tumor diagnosis and treatment planning, making accurate automated classification critically important. However, existing deep learning approaches face limitations in prediction reliability, generalization across imaging domains, and calibrated...

Md Ariful Islam Mozumder, Proloy Kumar Mondal, Hee-Cheol Kim et al. · Diagnostics · 0 citations

#machine learning Preprint Sep 2026

HyperAMS-Net: Adaptive Multi-Scale Spatial Hypergraph Network for Brain Disorder Classification

HyperAMS-Net is proposed, a deep learning framework for brain disorder classification using neuroimaging representations derived from resting-state functional MRI or structural MRI that integrates adaptive multi-scale convolution, hypergraph attention, spatial-channel attention, and adaptive feature fusion.

Proloy Kumar Mondal, Md Kamran Hussin Chowdhury, Hoi Leong Lee · 0 citations