An improved method for diagnosis of Parkinson’s disease using deep learning models enhanced with metaheuristic algorithm | BMC Medical Imaging | Full Text

Parkinson's disease (PD) is challenging for clinicians to accurately diagnose in the early stages. Quantitative measures of brain health can be obtained safely and non-invasively using medical imaging techniques like magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT). For accurate diagnosis of PD, powerful machine learning and deep learning models as well as the effectiveness of medical imaging tools for assessing neurological health are required. This study proposes four deep learning models with a hybrid model for the early detection of PD. For the simulation study, two standard datasets are chosen. Further to improve the performance of the models, grey wolf optimization (GWO) is used to automatically fine-tune the hyperparameters of the models. The GWO-VGG16, GWO-DenseNet, GWO-DenseNet + LSTM, GWO-InceptionV3 and GWO-VGG16 + InceptionV3 are applied to the T1,T2-weighted and SPECT DaTscan datasets. All the models performed well and obtained near o

An improved method for diagnosis of Parkinson’s disease using deep learning models enhanced with metaheuristic algorithm Research Open access Published: 24 June 2024 Volume 24 , article number 156 ( 2024 ) Cite this article You have full access to this open access article Download PDF Save article View saved research BMC Medical Imaging Aims and scope Submit manuscript An improved method for diagnosis of Parkinson’s disease using deep learning models enhanced with metaheuristic algorithm Download PDF Abstract Parkinson's disease (PD) is challenging for clinicians to accurately diagnose in the

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