In order to utilize this project, you will need to install the ACDC dataset on your local device. The dataset is not provided as part of the project package, so you will have to obtain it separately. The folder in the project directory named "preprocessed" will be automatically generated. This folder will be used to store preprocessed data generated during the course of the project.
Within the dataset, you may come across files with the extension "*.pt". These files are not particularly interesting for the purposes of this project. Therefore, you can safely ignore them and focus on the other relevant files.
It is worth noting that this project included a exploration process, therefore, CrossValidation.ipynb was not officially implemented in the end.
The entire deep learning algorithm, denoted as DeeplearningModels_Jaro.ipynb, can be found within the project. This algorithm serves as the core component for various tasks and computations. It encompasses a series of neural network models and associated training procedures to tackle the problem at hand.
In an effort to assist less experienced Git users, a comprehensive guide, denoted as Git.ipynb , was included in the project repository. This guide provides step-by-step instructions and best practices for utilizing Git and GitHub effectively.
As part of the project, two preprocessing files have been provided: preprocessing.ipynb and preprocessing_ROI.ipynb. These files handle the initial data preprocessing tasks necessary for preparing the dataset for subsequent analysis and modeling.
Please refer to the documentation and code comments within these preprocessing files for more detailed information on their functionality and usage.