Ensemble of Deep Transfer Learning Models for Parkinson's Disease Classification

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AbstractDiagnosis is the key step forward to cure a disease. Deep learning is becoming popular as a tool for usage in medical diagnosis. The existing literature using deep learning for the diagnosis of Parkinson’s disease (PD) by transfer learning of MRI data was limited to the AlexNet architecture. The present work aims to inculcate commonly used deep learning architectures using transfer learning for effective diagnosis of PD using MRI data. The best three performing models are selected based on the standard metric called F1-score. An ensemble model is proposed based on the maximum probability across all the selected models for PD classification. The approach mainly focuses on the effective diagnosis of PD. The performance of the proposed ensemble approach is validated using the standard metrics known as F1-score and classification accuracy. Among the commonly used deep learning architectures, it was found that VGG19 is better than the existing state-of-the-art, which is AlexNet. The proposed ensemble approach applied on top three commonly used deep learning models led to the improvement in accuracy to 0.978. The false positive (FP) and false negative (FN) were reduced significantly by the proposed ensemble approach.KeywordsDeep learningTransfer learningEnsemble methodParkinson’s diseaseCNN