A federated learning approach for detecting Parkinson’s disease through privacy preserving by blockchain

Chakrabarty, AmitabhaReza, Md. TanzimDipro, Sumit HowladerIslam, MynulNahian, Md.Abdullah AlAzad, Moonami Sharmita2022-11-152022-11-1520222022-05ID 18101154ID 18101155ID 17301102ID 16201039http://hdl.handle.net/10361/17568Cataloged from PDF version of thesis.Includes bibliographical references (pages 55-59).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.Parkinson’s disease is a degenerative ailment caused by the loss of nerve cells in the brain region known as the Substantia Nigra, which governs movement. These nerve cells die or deteriorate, rendering them unable to produce an essential neurotransmitter called dopamine. The loss of dopamine in the basal ganglia precludes normal function when the substantia nigra neurons are harmed in large numbers. This results in the motor symptoms of Parkinson’s disease, including tremor, rigidity, decreased balance, and lack of spontaneous movement. For the detection of PD, traditional machine learning algorithms have been used in many research papers. However, traditional ML algorithms always put a risk on the sensitivity of patients’ data privacy. This research proposes a novel approach to detect PD by preserving privacy and security through Blockchain-based Federated Learning. FL may train a single algorithm across numerous decentralized local servers as an improved version of the ML approach instead of trading gradient information. Blockchain can be effectively used to preserve privacy and secure transactions (i.e., gradient) between local and central servers. The proposed model has been tested and evaluated by using three CNN models (VGG19, VGG16 & InceptionV3) in this research, and within these models VGG19 has the best accuracy of 97%. The result demonstrates that this model is very accurate for detecting PD by preserving one’s privacy and security through Blockchain-based Federated Learning.59 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Parkinson’s diseaseFederated learningHealthcareBlockchainPrivacy preservingNeural networks (Computer science)Machine learningComputer algorithmsData encryption (Computer science)A federated learning approach for detecting Parkinson’s disease through privacy preserving by blockchainThesis