Pith. sign in

REVIEW 1 cited by

A Convolutional-based Model for Early Prediction of Alzheimer's based on the Dementia Stage in the MRI Brain Images

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2302.01417 v2 pith:ESIZ7U5T submitted 2023-02-02 cs.LG cs.CV

Shrish Pellakur , Nelly Elsayed , Zag ElSayed , Murat Ozer This is my paper · ORCID
classification cs.LGcs.CV
keywords alzheimerdiseasebraindementiaearlymodelstageadults
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Alzheimer's disease is a degenerative brain disease. Being the primary cause of Dementia in adults and progressively destroys brain memory. Though Alzheimer's disease does not have a cure currently, diagnosing it at an earlier stage will help reduce the severity of the disease. Thus, early diagnosis of Alzheimer's could help to reduce or stop the disease from progressing. In this paper, we proposed a deep convolutional neural network-based model for learning model using to determine the stage of Dementia in adults based on the Magnetic Resonance Imaging (MRI) images to detect the early onset of Alzheimer's.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Pre-trained Under Noise: A Framework for Robust Bone Fracture Detection in Medical Imaging

    eess.IV 2025-07 conditional novelty 4.0 of 10

    A comparison of pre-trained CNNs on noisy X-ray fracture detection finds VGG16 far more robust than ResNet50 or EfficientNet, suggesting a complexity-robustness tradeoff.

Pith tools