The dataset for testing was generated by a random split of 20% of the entire set, which didn’t participate in training process. A 32-image dataset was generated for testing, among which there were 7 MRI images of active phase and 25 MRI of inactive phase. K-fold cross-validation was employed to the remaining dataset (training set) for hyper-parameter tuning (k = 5). This algorithm divided the data into k folds randomly without replacement. During one cross-validation process, k-1 folds were used for training, and the remaining fold was used for validation. This process was then repeated k times to use each of the k folds once for validation. After finding the satisfactory hyper-parameters through K-fold cross-validation, A new model was trained from the whole training set.

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