Growing and Pruning Based Deep Neural Networks Modeling for Effective Parkinson’s Disease Diagnosis
Yazarlar (1)
Prof. Dr. Kemal AKYOL Kastamonu Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | CMES Computer Modeling in Engineering and Sciences (Q2) | ||
| Dergi ISSN | 1526-1492 Dergi Bilgileri (2020) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 02-2020 |
| Kabul Tarihi | 18-09-2019 | Yayınlanma Tarihi | 01-01-2020 |
| Cilt / Sayı / Sayfa | 122 / 2 / 619–632 | DOI | 10.32604/cmes.2020.07632 |
| Makale Linki | https://www.techscience.com/CMES/v122n2/38316 | ||
| UAK Araştırma Alanları |
Görüntü İşleme
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| Özet |
| Parkinson's disease is a serious disease that causes death. Recently, a new dataset has been introduced on this disease. The aim of this study is to improve the predictive performance of the model designed for Parkinson's disease diagnosis. By and large, original DNN models were designed by using specific or random number of neurons and layers. This study analyzed the effects of parameters, i.e., neuron number and activation function on the model performance based on growing and pruning approach. In other words, this study addressed the optimum hidden layer and neuron numbers and ideal activation and optimization functions in order to find out the best Deep Neural Networks model. In this context of this study, several models were designed and evaluated. The overall results revealed that the Deep Neural Networks were significantly successful with 99.34% accuracy value on test data. Also, it presents … |
| Anahtar Kelimeler |
| Deep neural networks | Growing and pruning | Machine learning | Parkinson’s disease |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 3 |
| Google Scholar | 5 |