arXiv Machine Learning
Aug 13
A comparison of CNN architectures for Alzheimer's disease detection in single-view MRI scans
arXiv:2608. 11762v1 Announce Type: cross Abstract: Alzheimer's disease is a leading cause of death with no cure.
How Robust Are CNNs and Vision Transformers for Alzheimer's MRI Classification Across Datasets?
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arXiv AI
Jun 30
A Deep Multiscale Neural Network for Accurate Neurological Disorder Detection from MRI Scans and Real-Time Web Deployment
arXiv:2606. 29106v1 Announce Type: cross Abstract: Neurological disorders involve diverse pathologies of the brain and nervous system, making early and accurate detection essential.
arXiv AI
Jul 16
Beyond Backbone Backpropagation: A Decoupled Strategy for Efficient Transfer Learning
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.
arXiv AI
Jun 29
Automated brain tumor detection in MRI images using CNN and ResNet architectures
arXiv:2606. 27405v1 Announce Type: cross Abstract: Deep learning has shown significant potential in medical image analysis, particularly for disease detection using MRI scans.
arXiv Computer Vision
Oct 9
A Two-Scan Deep Learning Model for Predicting Dementia in Mild Cognitive Impairment
The paper introduces TAFNet, a temporal attention fusion network that uses both a baseline and a follow‑up T1‑weighted brain scan to predict which individuals with mild cognitive impairment (MCI) will progress to dementia. The model employs a pretrained Siamese encoder for each scan and fuses the two scans through anatomical difference, cross‑temporal attention, joint context, and a learned per‑patient gate. Evaluated on paired scans from the Alzheimer’s Disease Neuroimaging Initiative, TAFNet outperforms single‑scan networks and a simple scan‑difference model, achieving significant gains in cross‑validation and maintaining high sensitivity at clinically reasonable specificity.
Hugging Face Trending Papers
Jun 27
A Deep Multiscale Neural Network for Accurate Neurological Disorder Detection from MRI Scans and Real-Time Web Deployment
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 AI
Sep 15
Anatomical Grounding and Leakage-Aware Multimodal Contrastive Learning for Alzheimer's Disease Classification from Structural MRI
arXiv:2609.15888v1 Announce Type: cross Abstract: Deep networks trained on structural MRI for Alzheimer's disease (AD) staging often reach reasonable accuracy while attending to anatomically irreleva...
Hugging Face Trending Papers
Jun 21
Multi-cancer detection using a computationally efficient CNN with transfer learning
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.
arXiv Computer Vision
4d ago
EigenGradCAM with AugSmooth: Enhancing Explainability and Precision in Multi-Stage Alzheimer's Disease Detection
arXiv:2610.32374v1 Announce Type: new Abstract: Alzheimer's disease (AD) is a progressive neurological disorder associated with memory loss, cognitive decline, and behavioral disturbances. Early and...
arXiv AI
Sep 1
ActiveAugment: Online Active Learning for Augmentation Selection in Deep Learning
arXiv:2608.28923v1 Announce Type: cross Abstract: Data augmentation is a cornerstone of deep learning pipelines, yet existing strategies treat it as a static, model-agnostic preprocessing step, eithe...
arXiv AI
Aug 28
Pixel Wised Lesion Prediction on COVID-19 CT Imagery: A Comparative Analysis of Automated Image Segmentation Architectures
The study evaluates four deep‑learning segmentation architectures—Unet, PSPNet, Linknet, and FPN—paired with six pre‑trained encoders to predict COVID‑19 lesions in CT images. Experiments on three COVID‑19 CT datasets show high accuracy, achieving a maximum binary F1‑score of 98% and multi‑class F1‑scores of 75% and 77%. The work aims to provide a standardized performance benchmark for medical image segmentation and a reference for other imaging scenarios.
arXiv Computer Vision
Sep 25
Does DCGAN-Based Synthetic Augmentation Improve Brain Tumor MRI Classification? An Empirical Study
This study examined whether augmenting brain tumor MRI datasets with class‑specific DCGAN‑generated images improves classification performance. Using 7,200 scans across four tumor categories, a Swin Transformer classifier trained on real images alone achieved 96% accuracy, identical to the model trained with 500 synthetic images per class. Metrics such as macro F1 and ROC‑AUC showed no improvement, and FID scores indicated substantial distributional differences between real and synthetic images.