A deep learning approach for the early diagnosis of Parkinson's disease using brain MRI scans

Mishra, Rishik; Jalal, Anand Singh; Kumar, M. Pawan; Jalal, Sunita

doi:10.1504/ijapr.2022.122269

Cited by 3 publications

(2 citation statements)

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“…They showed that pre-training the model on a large-scale speech recognition task significantly improved the accuracy of PD detection. Similarly [ 145 ], utilized transfer learning to improve the performance of a CNN-based model for PD diagnosis using PET images.…”

Section: Future Directionsmentioning

confidence: 99%

A review of machine learning and deep learning algorithms for Parkinson's disease detection using handwriting and voice datasets

Islam,

Hasan Majumder,

Hussein

et al. 2024

Heliyon

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Section: Future Directionsmentioning

confidence: 99%

A review of machine learning and deep learning algorithms for Parkinson's disease detection using handwriting and voice datasets

Islam,

Hasan Majumder,

Hussein

et al. 2024

Heliyon

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“…General classification applied directly to LSTM does not produce specific results. Therefore, it is an excellent strategy to use a hybrid model combining a ResNet (CNN) with LSTM to have more accurate results[35,36]. The ResNet (CNN) LSTM model utilizes ResNet layers for learning features to join the LSTM layer to help accurate prediction.…”

mentioning

confidence: 99%

Automatic Detection of Atrial Fibrillation from ECG Signal Using Hybrid Deep Learning Techniques

Pandey

Kumar

Shukla³

et al. 2022

Journal of Sensors

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In cardiac rhythm disorders, atrial fibrillation (AF) is among the most deadly. So, ECG signals play a crucial role in preventing CVD by promptly detecting atrial fibrillation in a patient. Unfortunately, locating trustworthy automatic AF in clinical settings remains difficult. Today, deep learning is a potent tool for complex data analysis since it requires little pre and postprocessing. As a result, several machine learning and deep learning approaches have recently been applied to ECG data to diagnose AF automatically. This study analyses electrocardiogram (ECG) data from the PhysioNet/Computing in Cardiology (CinC) Challenge 2017 to differentiate between atrial fibrillation (AF) and three other rhythms: normal, other, and too noisy for assessment. The ECG data, including AF rhythm, was classified using a novel model based on a combination of traditional machine learning techniques and deep neural networks. To categorize AF rhythms from ECG data, this hybrid model combined a convolutional neural network (Residual Network (ResNet)) with a Bidirectional Long Short Term Memory (BLSTM) network and a Radial Basis Function (RBF) neural network. Both the F1-score and the accuracy of the final hybrid model are relatively high, coming in at 0.80% and 0.85%, respectively.

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FPGA-enhanced IoT methods for disease pre-screening and prediction: An energy optimization approach

Satpathy,

Trivedi,

Malhotra

et al. 2024

Measurement: Sensors

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A deep learning approach for the early diagnosis of Parkinson's disease using brain MRI scans

Cited by 3 publications

References 0 publications

A review of machine learning and deep learning algorithms for Parkinson's disease detection using handwriting and voice datasets

A review of machine learning and deep learning algorithms for Parkinson's disease detection using handwriting and voice datasets

Automatic Detection of Atrial Fibrillation from ECG Signal Using Hybrid Deep Learning Techniques

FPGA-enhanced IoT methods for disease pre-screening and prediction: An energy optimization approach

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