Alzheimers Detection

• Total of 4 notebooks, each for different parts of the project
• Notebooks has different sections to organize codes

Notebooks

• Generate_Transcripts.ipynb

  • Contains the code used to generate text transcripts using HuBERT

• Audio_Classification.ipynb

  • Install dependencies: Install openSMILE

  • File preprocessing functions Change labels to integer

  • Functions to extract features: Functions extract eGeMAPS features

  • Extracting features: Extracting eGeMAPS features and saving to a csv file

  • Load features and data preprocessing: Loading features from csv file and performing normalization

  • Machine Learning classifiers: This section has functions to train different classifiers with 10 fold grid search cv (Decision Tree, Decision Tree Bagger, Random Forest, Random Forest Bagger, Support Vector, Logistic Regression)

  • Neural Network: This section contains codes used to create the 4-layer network and do 10 fold grid search cv

• Text_Classification.ipynb

  • Load text transcripts: Load the generated text transcripts

  • Data Preprocessing: Text data preprocessing, to lowercase all text

  • Doc2Vec: Contains codes used to perform grid search 10 fold cross validation for machine learning classifiers and a neural network using Doc2Vec embeddings

  • BERT: Contains codes to load BERT model, extract embeddings, training and tuning of neural network model

• Fusion_Mechanisms.ipynb

  • Load pre-trained text model: Loading the text data and some preprocessing, and loading the text model

  • Load pre-trained audio model: Loading audio data and audio model

  • Get audio and text inputs: More data preprocessing and to get audio and text data inputs

  • Bilinear Pooling: Has classes to create a bilinear pooling layer, function to create the bilinear pooling model, and training of the model with 10 fold cv

  • Concatenation: Has function to create the model with concatenate layer and training of the model with 10 fold cv