Alzheimer's detection using quanvolutional neural networks with federated training
Prakhar Mishra, Subitha D., Pramukh Rajendra Jain, Arogya Sharma, Kavitha J. C. · 2026
Read Alzheimer's detection using quanvolutional neural networks with federated training by Prakhar Mishra, Subitha D., Pramukh Rajendra Jain, Arogya Sharma, Kavitha J. C. (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13646070.txt.
Description
Prakhar Mishra and four colleagues build a system to detect Alzheimer's disease from brain scans while keeping patient data private. Their method mixes a small quantum computing front end with a classical neural network. Scan images are broken into tiny patches, and quantum circuits extract features before classification. Training is spread across separate sites with a method called federated learning, so raw scans never leave each site.
The combined approach reached accuracy just above ninety-two percent in its best federated setup. A privacy layer that adds controlled noise still kept accuracy near ninety-one percent. With two hundred simulated clients, one averaging method held up far better than the other. The authors present the frozen quantum front end as a way to gain quantum feature power without heavy communication costs. The work points toward hospital networks training shared models without pooling private scans.
Catalog record
| CREATOR | Prakhar Mishra, Subitha D., Pramukh Rajendra Jain, Arogya Sharma, Kavitha J. C. |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 63 KB |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |