PDDS: A software for the early diagnosis of Parkinson’s disease from MRI and DaT scan images using detection and segmentation algorithms

Tassew, Tewodros Megabiaw; Nie, Xuan; Chai, Bosong

doi:10.1016/j.bspc.2023.105140

Cited by 1 publication

(1 citation statement)

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“…A result of up to 97% accuracy at the individual level is achieved using a support vector machine (SVM) analysis of a diffusion tensor image (DTI). There are some studies that have used deep transfer learning in the analysis of DaTscan in the last three years, where DaTScan is injected into the blood and, using special imaging equipment, scans the head for detection [14]. DatScan imaging can also be used to diagnose PD, even when patients are in a very early stage with less common parkinsonism clinical presentations [15].…”

Section: Introductionmentioning

confidence: 99%

Early Parkinson’s Disease Diagnosis through Hand-Drawn Spiral and Wave Analysis Using Deep Learning Techniques

Huang,

Chaturvedi,

Nayan

et al. 2024

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Parkinson’s disease (PD) is a chronic brain disorder affecting millions worldwide. It occurs when brain cells that produce dopamine, a chemical controlling movement, die or become damaged. This leads to PD, which causes problems with movement, balance, and posture. Early detection is crucial to slow its progression and improve the quality of life for PD patients. This paper proposes a handwriting-based prediction approach combining a cosine annealing scheduler with deep transfer learning. It utilizes the NIATS dataset, which contains handwriting samples from individuals with and without PD, to evaluate six different models: VGG16, VGG19, ResNet18, ResNet50, ResNet101, and Vit. This paper compares the performance of these models based on three metrics: accuracy, precision, and F1 score. The results showed that the VGG19 model, combined with the proposed method, achieved the highest average accuracy of 96.67%.

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Section: Introductionmentioning

confidence: 99%