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Archivio digitale delle tesi discusse presso l’Università di Pisa

Tesi etd-09122024-152715


Tipo di tesi
Tesi di laurea magistrale
URN
etd-09122024-152715
Titolo
Convolutional Deep Learning for multimodal classification of abnormal electrical brain activity
Dipartimento
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
ARTIFICIAL INTELLIGENCE AND DATA ENGINEERING
Parole chiave
  • brain activity
  • brain seizures
  • cnn
  • deep learning
  • eeg
  • electroencephalograms
  • elettroencefalogrammi
  • epilepsy
  • epilessia
  • patologie cerebrali
Data inizio appello
07/10/2024
Consultabilità
Non consultabile
Data di rilascio
07/10/2027
Riassunto (Inglese)
Riassunto (Italiano)
Using medical image data, this thesis will focus on the adoption of deep learning models for harmful brain activity classification. In particular, a detection and classification of anomalies related to electroencephalograms (EEGs) will be performed. Convolutional neural networks (CNNs) will be trained and fine-tuned on a dataset provided by a medical centre, with the aim of improving the identification of pathological brain patterns and contributing to faster and more accurate diagnoses.
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