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.
arXiv:2608. 11762v1 Announce Type: cross Abstract: Alzheimer's disease is a leading cause of death with no cure.
By Hiram Zuniga, Ulises Orozco-Rosas, Kenia Picos
arXiv:2606. 27405v1 Announce Type: cross Abstract: Deep learning has shown significant potential in medical image analysis, particularly for disease detection using MRI scans.
By Annapurna V K, Asha N, K Paramesha, Shabana Sultana, Kirankumar Humse
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
arXiv:2607. 13043v1 Announce Type: cross Abstract: Deep learning models achieve state-of-the-art image classification but face deployment challenges due to computational costs and energy demands.
By Daniel Vila-Cruz, Laura Mor\'an-Fern\'andez, Ver\'onica Bol\'on-Canedo
arXiv:2606. 01293v1 Announce Type: cross Abstract: Accurate segmentation of fetal brain tissues in Magnetic Resonance Imaging (MRI) is critical for early diagnosis of congenital abnormalities and improving prenatal care.
By Ashiqur Rahman, Muhammad E. H. Chowdhury, Md. Abu Sayed, Md. Sharjis Ibne Wadud, Abu Naser Md. Arafat, Mehedi Hasan Prince
arXiv:2610.00279v1 Announce Type: new
Abstract: The segmentation of anatomical structures in medical images and particularly in MRI scans, is essential for clinical diagnosis and monitoring disease p...
By Eirini Cholopoulou, Dimitrios E. Diamantis, Dimitris K. Iakovidis
arXiv:2606. 11107v1 Announce Type: cross Abstract: Clinicians diagnose brain tumors by synthesizing patient symptoms, medical history, and quantitative imaging data from modalities such as MRI and CT scans into a unified clinical judgement.
By Wajih ul Islam, Muhammad Yaqoob, Javed Ali Khan, Volker Steuber
The study evaluated a pragmatic deep‑learning approach for segmenting acute ischemic stroke lesions on diffusion‑weighted MRI. Using a self‑configured nnU‑Net trained on 1,744 cases and tested on 436, the baseline model achieved a median Dice similarity coefficient of 0.84, outperforming the DeepISLES ensemble, especially for smaller infarcts. The approach required minimal preprocessing and fast inference, suggesting it could streamline clinical stroke imaging workflows.
By Atle Bj{\o}rnerud, Till Schellhorn, Thor H. Skatt{\o}r, Terje Nome, Jon Andr\'e Ottesen, Anne Hege Aamodt, Bradley J MacIntosh
arXiv:2606. 20037v1 Announce Type: new Abstract: Alzheimer's disease (AD) is an irreversible neurodegenerative disorder and a leading cause of death worldwide.
By Loukas Ilias, Anthi-Maria Vozinaki, Christos Ntanos, Dimitris Askounis
This study introduces a computationally efficient convolutional neural network (CNN) architecture enhanced with transfer learning for multi-cancer detection using biomedical images. The proposed lightweight CNN model is designed to reduce computational complexity while maintaining high classification performance, making it suitable for deployment in resource-constrained environments.
The paper presents a multimodal 3D convolutional neural network that classifies Alzheimer’s disease using raw OASIS 1 MRI volumes. It fuses structural T1 images with gray matter, white matter, and cerebrospinal fluid probability maps to capture complementary neuroanatomical information. Evaluated with 5‑fold subject‑level cross‑validation, the model achieves a mean accuracy of 72.34 % and an ROC AUC of 0.7781, with GradCAM visualizations highlighting anatomically relevant regions such as the medial temporal lobe and ventricles.
By Md Sifat, Sania Akter, Akif Islam, Md. Ekramul Hamid, Abu Saleh Musa Miah, Najmul Hassan, Md Abdur Rahim, Jungpil Shin