Abstract
In the present work, we introduce a data processing and analysis pipeline, which ensures the reproducibility of machine learning models chosen for MR image recognition. The proposed pipeline is applied to solve the binary classification problems: epilepsy and depression diagnostics based on vectorized features from MR images. This model is then assessed in terms of classification performance, robustness and reliability of the results, including predictive accuracy on unseen data. The classification performance achieved with our approach compares favorably to ones reported in the literature, where usually no thorough model evaluation is performed.