000			02820nam a22003017a 4500
003			KOHA
005			20230713105946.0
008			230713d2023 cy ||||| m||| 00| 0 eng d
040			_aCY-NiCIU _beng _cCY-NiCIU _erda
041			_aeng
090			_aYL 2931 _bA48 2023
100	1		_aAgunta, Wisdom Chigozie
245	1	0	_aDEEP LEARNING APPROACH USING CNN MODELS TOWARDS EFFECTUAL TREATMENT OF PARKINSON DISEASE / _cWISDOM CHIGOZIE AGUNTA SUPERVISOR ASSOC. PROF. DR. HÜSEYIN ÖZTOPRAK
264			_c2023
300			_axiii, 65 sheets; _c31 cm. _eIncludes CD
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
502			_aThesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Information Technologies Department
504			_aIncludes bibliography (sheets 60-65)
520			_aABSTRACT 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.
650		0	_aDeep learning (Machine learning) _vDissertations, Academic
650		0	_aPrecision medicine _vDissertations, Academic
650		0	_aTherapeutics _vDissertations, Academic
650		0	_aParkinson's disease _vDissertations, Academic
700	1		_aÖztoprak, Hüseyin _esupervisor
942			_2ddc _cTS
999			_c290578 _d290578