ArticleFrontiers in aging neuroscience2022
Soft Attention Based DenseNet Model for Parkinson's Disease Classification Using SPECT Images.
Article in Frontiers in aging neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Detection of parkinson's disease with neuroimaging modalities using machine learning and artificial intelligence: a systematic review.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Pooled it
- Using Convolutional Neural Networks for the Classification of Suboptimal Chest Radiographs.Journal of medical radiation sciences · 2026Article
- FedMal-XAI: an explainable federated vision transformer leveraging knowledge distillation for privacy-preserving malaria detection.Frontiers in public health · 2026Article
- Enhancing the Predictions of Cytomegalovirus Infection in Severe Ulcerative Colitis Using a Deep Learning Ensemble Model: Development and Validation Study.JMIR medical informatics · 2025Article
- Fully automatic categorical analysis of striatal subregions in dopamine transporter SPECT using a convolutional neural network.Annals of nuclear medicine · 2025Article
- Prediction of 123I-FP-CIT SPECT Results from First Acquired Projections Using Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2025Article
- FCN-PD: An Advanced Deep Learning Framework for Parkinson's Disease Diagnosis Using MRI Data.Diagnostics (Basel, Switzerland) · 2025Article
- Incorporating label uncertainty during the training of convolutional neural networks improves performance for the discrimination between certain and inconclusive cases in dopamine transporter SPECT.European journal of nuclear medicine and molecular imaging · 2025Article
- Alzheimer's disease image classification based on enhanced residual attention network.PloS one · 2025Article
- Hybrid-RViT: Hybridizing ResNet-50 and Vision Transformer for Enhanced Alzheimer's disease detection.PloS one · 2025Article
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- Hyperparameter tuned deep learning-driven medical image analysis for intracranial hemorrhage detection.PloS one · 2025Article
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- Convolutional Neural Network-Machine Learning Model: Hybrid Model for Meningioma Tumour and Healthy Brain Classification.Journal of imaging · 2024Article
- Radiomics incorporating deep features for predicting Parkinson's disease inEJNMMI physics · 2024Article
- An improved method for diagnosis of Parkinson's disease using deep learning models enhanced with metaheuristic algorithm.BMC medical imaging · 2024Article
- Optimizing Machine Learning Models for Accessible Early Cognitive Impairment Prediction: A Novel Cost-effective Model Selection Algorithm.IEEE access : practical innovations, open solutions · 2024Article
- Bacterial image analysis using multi-task deep learning approaches for clinical microscopy.PeerJ. Computer science · 2024Article
- A review of emergent intelligent systems for the detection of Parkinson's disease.Biomedical engineering letters · 2023Review
- An Improved Method for Diagnosis of Parkinson's Disease using Deep Learning Models Enhanced with Metaheuristic Algorithm.Research square · 2023Article
Corrections and comments
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5 authors.
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Abstract
Objective: Deep learning algorithms have long been involved in the diagnosis of severe neurological disorders that interfere with patients' everyday tasks, such as Parkinson's disease (PD). The most effective imaging modality for detecting the condition is DaTscan, a variety of single-photon emission computerized tomography (SPECT) imaging method. The goal is to create a convolutional neural network that can specifically identify the region of interest following feature extraction. Methods: The study comprised a total of 1,390 DaTscan imaging groups with PD and normal classes. The architecture of DenseNet-121 is leveraged with a soft-attention block added before the final classification layer. For visually analyzing the region of interest (ROI) from the images after classification, Soft Attention Maps and feature map representation are used. Outcomes: The model obtains an overall accuracy of 99.2% and AUC-ROC score 99%. A sensitivity of 99.2%, specificity of 99.4% and f1-score of 99.1% is achieved that surpasses all prior research findings. Soft-attention map and feature map representation aid in highlighting the ROI, with a specific attention on the putamen and caudate regions. Conclusion: With the deep learning framework adopted, DaTscan images reveal the putamen and caudate areas of the brain, which aid in the distinguishing of normal and PD cohorts with high accuracy and sensitivity.
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