Papers › Automated Brain Disorders Diagnosis Through Deep Neural Networks
Automated Brain Disorders Diagnosis Through Deep Neural Networks
Gabriel Maggiotti
In most cases, the diagnosis of brain disorders such as epilepsy is slow and requires endless visits to doctors and EEG technicians. This project aims to automate brain disorder diagnosis by using Artificial Intelligence and deep learning. Brain could have many disorders that can be detected by reading an Electroencephalography. Using an EEG device and collecting the electrical signals directly from the brain with a non-invasive procedure gives significant information about its health. Classifying and detecting anomalies on these signals is what currently doctors do when reading an Electroencephalography. With the right amount of data and the use of Artificial Intelligence, it could be possible to learn and classify these signals into groups like (i.e: anxiety, epilepsy spikes, etc). Then, a trained Neural Network to interpret those signals and identify evidence of a disorder to finally automate the detection and classification of those disorders found.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections