ArticleBMC medical imaging2024
An improved method for diagnosis of Parkinson's disease using deep learning models enhanced with metaheuristic algorithm.
Article in BMC medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Deep learning-based cognitive impairment brain imaging analysis: New methods, new technologies, and new paradigms.Neural regeneration research · 2026Article
- Machine Learning-Driven Radiomic Profiling of Thalamus-Amygdala Nuclei for Prediction of Postoperative Delirium After STN-DBS in Parkinson's Disease Patients: A Pilot Study.Journal of imaging informatics in medicine · 2026Article
- Adaptive regression model for Parkinson's disease diagnosis from speech signals using Box-Cox-based clustering and extremely randomization.Scientific reports · 2026Article
- Hybrid deep learning novel framework for classification of parkinson's disease.Scientific reports · 2026Article
- Early Diagnosis of Parkinson's Disease Through Lite HGWA-Net Model: A Hybrid CNN Based on Wavelet Transform and Attention Mechanism.Diagnostics (Basel, Switzerland) · 2026Article
- Structural and Statistical Knowledge-Enhanced Attention Network for early Parkinson's disease diagnosis.Quantitative imaging in medicine and surgery · 2026Article
- Convolutional neural networks in brain disease diagnosis: a unified review of Alzheimer's, Parkinson's, and brain tumor classification.Frontiers in neuroscience · 2026Review
- A quantum-classical dual-track deep learning network for explainable Parkinson's disease classification.Frontiers in artificial intelligence · 2026Article
- FCN-PD: An Advanced Deep Learning Framework for Parkinson's Disease Diagnosis Using MRI Data.Diagnostics (Basel, Switzerland) · 2025Article
- Article
- Classification of Parkinson's disease by deep learning on midbrain MRI.Frontiers in aging neuroscience · 2024Article
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7 authors.
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Abstract
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 or above 99% accuracy. The highest accuracy of 99.94% and AUC of 99.99% is achieved by the hybrid model (GWO-VGG16 + InceptionV3) for T1,T2-weighted dataset and 100% accuracy and 99.92% AUC is recorded for GWO-VGG16 + InceptionV3 models using SPECT DaTscan dataset.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.