Role of artificial intelligence techniques and neuroimaging modalities in detection of Parkinson's disease: a systematic review
Abstract Parkinson's disease (PD), a neurodegenerative disorder, is caused due to the lack
of dopamine neurotransmitters throughout the substantia nigra. Its diagnosis in the earlier …
of dopamine neurotransmitters throughout the substantia nigra. Its diagnosis in the earlier …
A computerized analysis with machine learning techniques for the diagnosis of Parkinson's disease: past studies and future perspectives
According to the World Health Organization (WHO), Parkinson's disease (PD) is a
neurodegenerative disease of the brain that causes motor symptoms including slower …
neurodegenerative disease of the brain that causes motor symptoms including slower …
PARNet: Deep neural network for the diagnosis of parkinson's disease
In this study, the successful network architecture we developed from scratch to diagnose
COVID-19 has been retrained, using single photon emission computed tomography …
COVID-19 has been retrained, using single photon emission computed tomography …
A review of emergent intelligent systems for the detection of Parkinson's disease
S Dhanalakshmi, RS Maanasaa, RS Maalikaa… - Biomedical Engineering …, 2023 - Springer
Parkinson's disease (PD) is a neurodegenerative disorder affecting people worldwide. The
PD symptoms are divided into motor and non-motor symptoms. Detection of PD is very …
PD symptoms are divided into motor and non-motor symptoms. Detection of PD is very …
An improved method for diagnosis of Parkinson's disease using deep learning models enhanced with metaheuristic algorithm
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 …
stages. Quantitative measures of brain health can be obtained safely and non-invasively …
Enhanced Parkinson's Disease Diagnosis Through Convolutional Neural Network Models Applied to SPECT DaTSCAN Images
Convolutional Neural Networks (CNNs) are highly regarded in Deep Learning (DL) and
have shown promising results in medical image analysis, making them a leading model for …
have shown promising results in medical image analysis, making them a leading model for …
An Ensemble of Deep Learning Object Detection Models for Anatomical and Pathological Regions in Brain MRI
R Terzi - Diagnostics, 2023 - mdpi.com
This paper proposes ensemble strategies for the deep learning object detection models
carried out by combining the variants of a model and different models to enhance the …
carried out by combining the variants of a model and different models to enhance the …
Parkinson's disease diagnosis from T1 and T2 weighted magnetic resonance images using FBLstmNet architecture
Parkinson's is a dreadful neurodegenerative disorder disease which affects people over the
age of 40 around the world. To reduce the risk factors of Parkinson's, early diagnosis is …
age of 40 around the world. To reduce the risk factors of Parkinson's, early diagnosis is …
Radiomics and Hybrid Models Based on Machine Learning to Predict Levodopa-Induced Dyskinesia of Parkinson's Disease in the First 6 Years of Levodopa …
Y Luo, H Chen, M Gui - Diagnostics, 2023 - mdpi.com
Background: Current research on the prediction of movement complications associated with
levodopa therapy in Parkinson's disease (PD) is limited. levodopa-induced dyskinesia (LID) …
levodopa therapy in Parkinson's disease (PD) is limited. levodopa-induced dyskinesia (LID) …
[PDF][PDF] A Hybrid CNN-LSTM Deep Learning Model for Classification of the Parkinson Disease.
RS El-Sayed - IAENG International Journal of Applied Mathematics, 2023 - iaeng.org
Parkinson disease (PD) is a degenerative disorder of neurological disorders that affects
movements, balance problems and more. Early prediction of PD means enhancing the …
movements, balance problems and more. Early prediction of PD means enhancing the …