Early Detection & Stage Classification of Parkinson’s Disease using Deep Learning / (Record no. 607199)

000 -LEADER
fixed length control field 01951nam a22001577a 4500
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 629.8
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Zeeshan, Muhammad Muzzamil
245 ## - TITLE STATEMENT
Title Early Detection & Stage Classification of Parkinson’s Disease using Deep Learning /
Statement of responsibility, etc. Muhammad Muzzamil Zeeshan
264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture Islamabad :
Name of producer, publisher, distributor, manufacturer SMME- NUST;
Date of production, publication, distribution, manufacture, or copyright notice 2023.
300 ## - PHYSICAL DESCRIPTION
Extent 38p. ;
Dimensions 30cm.
520 ## - SUMMARY, ETC.
Summary, etc. Parkinson’s disease (PD) is caused by a lack of dopamine production by the substantia nigra in<br/>the brain. It is an enduring disorder without any cure, making it a burden on the patient and the<br/>society. PD is a complex disorder marked by many physical and non-physical manifestations,<br/>which differ for everyone. Clinicians might misdiagnose, waste time and resources to get a<br/>patient’s diagnosis or do not have enough expertise to diagnose a patient. Deep learning models<br/>tend to overfit with new data; thus, to prevent variance in the model, merging outputs has been<br/>proven effective. This study proposes an ensemble deep learning model, to automate PD<br/>detection and stage classification, which can handle different data by combining rules. The<br/>ensemble model (TransConvNet) links two state-of-the art deep learning models in decision level<br/>ensembling. The outputs are fused using averaging voting. Both neural networks utilize gait data<br/>provided by Physionet. The validation accuracy for PD detection reached 82%, while for PD<br/>stage classification, it reached 73%. This model delivers competitive and top-notch performance<br/>for severity and detection prediction for PD using gait. This can be used as a tool for PD<br/>detection or monitoring its development. Future work might include addition of models for better<br/>performance and reducing the training time of the models.<br/>
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element MS Biomedical Engineering (BME)
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Supervisor : Dr. KASHIF JAVED
856 ## - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="http://10.250.8.41:8080/xmlui/handle/123456789/39590">http://10.250.8.41:8080/xmlui/handle/123456789/39590</a>
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Koha item type Thesis
Holdings
Withdrawn status Permanent Location Current Location Date acquired Full call number Barcode Koha item type
  School of Mechanical & Manufacturing Engineering (SMME) School of Mechanical & Manufacturing Engineering (SMME) 12/06/2023 610 SMME-TH-939 Thesis
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