Thesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Information Technologies Department

Includes bibliography (sheets 60-65)

ABSTRACT
This thesis proposal aims to explore the potential of utilizing convolutional neural
network, in particular, deep learning techniques (CNN) models, to improve the
efficacy of treatment options for individuals diagnosed with Parkinson's Disease (PD).
The proposed research will focus on developing and evaluating advanced CNN models
that can accurately predict the progression of PD and assist in the selection of
personalized treatment options. A comprehensive dataset of imaging and clinical data
from individuals with PD will be collected and used to train and evaluate the
performance of the proposed CNN models. The proposed research will also investigate
the incorporation of state-of-the-art CNN architectures such as MobileNet_v2 to
enhance the capabilities of the models in accurately predicting PD progression.
Accuracy, precision, recall, and F1 score will be used to objectively assess the models'
performance. The results will then be studied to ascertain whether CNN models have
the potential to enhance PD treatment options. The results of this investigation will
have important ramifications for the development of more accurate and personalized
treatment options for PD and will contribute to the ongoing efforts to improve the
management and treatment of this debilitating disorder.
Keywords: Clinical data, CNN models, Deep learning, Development, Effectual,
Imaging data, Implications, Management, MobileNet, MobileNet_v2, Ongoing
efforts, Personalized medicine, Personalized treatment, Parkinson's disease,
Progression prediction, Treatment.