Szczegóły publikacji
Opis bibliograficzny
MRI neuroimaging-based Alzheimer’s disease stage classification using deep neural network with convolutional block attention module and GAN-style noise injection / Sachin Kumar, Sourabh SHASTRI, Vibhakar Mansotra, Rohit SALGOTRA // Scientific Reports [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2045-2322 . — 2026 — vol. 16 art. no. 6946, s. 1–18. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 16–18, Abstr. — Publikacja dostępna online od: 2026-02-02. — S. Shastri – dod. afiliacja: Centre of Excellence in Artificial Intelligence, AGH University of Krakow. — R. Salgotra – dod. afiliacje: University of Technology Sydney, Australia
Autorzy (4)
- Kumar Sachin
- Shastri Sourabh
- Mansotra Vibhakar
- AGHSalgotra Rohit
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 167069 |
|---|---|
| Data dodania do BaDAP | 2026-04-24 |
| Tekst źródłowy | URL |
| DOI | 10.1038/s41598-026-37226-2 |
| Rok publikacji | 2026 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Scientific Reports |
Abstract
Millions of individuals worldwide suffer from Alzheimer's disease (AD), a chronic, incurable neurological disorder. For the longevity of people, a computer-aided system can contribute to the maximum possible extent. Recently, Deep-learning algorithms have shown better results than machine learning techniques. Researchers have applied CNN models on MRI datasets in various recent studies and have obtained promising results for the early detection of AD. This study proposes a Neuro_CBAM-ADNet diagnostic model for early prediction of the four stages of AD, using MRI digital images. The results show that the proposed model achieved mean accuracy of 98.28%+/- 0.31, which also outperforms the previous related works. The proposed model can identify AD without human intervention and also at an economical cost with high accuracy. The optimistic findings of deep learning models in the early diagnosis of illnesses like AD show that deep learning plays a crucial role in combating these neurological diseases.
