Journal ArticleDOI
An Improved Deep Polynomial Network Algorithm for Transcranial Sonography–Based Diagnosis of Parkinson’s Disease
TLDR
An improved DPN algorithm with enhanced performance on both feature representation and classification is proposed, and the proposed D-P-EKN-DPN algorithm has a great potential in TCS-based CAD for PD due to its excellent performance.Abstract:
Transcranial sonography (TCS) is a valid neuroimaging tool for the diagnosis of Parkinson’s disease (PD). The TCS-based computer-aided diagnosis (CAD) has attracted increasing attention in recent years, in which feature representation and pattern classification are two critical issues. Deep polynomial network (DPN) is a newly proposed deep learning algorithm that has shown its advantage in learning effective feature representation for samples with a small size. In this work, an improved DPN algorithm with enhanced performance on both feature representation and classification is proposed. First, the empirical kernel mapping (EKM) algorithm is embedded into DPN (EKM-DPN) to improve its feature representation. Second, the network pruning strategy is utilized in the EKM-DPN (named P-EKM-DPN). It not only produces robust feature representation, but also addresses the overfitting issues for the subsequent classifiers to some extent. Lastly, the generalization ability is further enhanced by applying the Dropout approach to P-EKM-DPN (D-P-EKM-DPN). The proposed D-P-EKM-DPN algorithm has been evaluated on a TCS dataset with 153 samples. The experimental results indicate that D-P-EKM-DPN outperforms all the compared algorithms and achieves the best classification accuracy, sensitivity, and specificity of 86.95 ± 3.15%, 85.77 ± 7.87%, and 87.16 ± 6.50%, respectively. The proposed D-P-EKN-DPN algorithm has a great potential in TCS-based CAD for PD due to its excellent performance.read more
Citations
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Journal ArticleDOI
A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis.
TL;DR: Deep learning has been used for the analysis of neuroimages, such as structural magnetic resonance imaging (MRI), functional MRI, and positron emission tomography (PET), and it has achieved significant performance improvements over traditional machine learning in computer-aided diagnosis of brain disorders as mentioned in this paper.
Journal ArticleDOI
A fuzzy-enhanced deep learning approach for early detection of Covid-19 pneumonia from portable chest X-ray images
Cosimo Ieracitano,Nadia Mammone,Mario Versaci,Giuseppe Varone,Abder-Rahman Ali,Antonio Armentano,Grazia Calabrese,Anna,Ferrarelli,Lorena Turano,Carmela Tebala,Zain Hussain,Zakariya,Sheikh,Aziz Sheikh,G. Sceni,Amir Hussain,Francesco Carlo,Morabito +18 more
TL;DR: In this article , a fuzzy logic based deep learning (DL) approach was proposed to differentiate between CXR images of patients with Covid-19 pneumonia and with interstitial pneumonias not related to Covid19.
Journal ArticleDOI
Deep learning for Alzheimer's disease diagnosis: A survey
TL;DR: In this article , the authors performed a comparative analysis of about 100 published papers since 2019 that employ basic deep architectures such as CNN, RNN, and generative models for AD diagnosis.
Journal ArticleDOI
Recognizing human behaviors from surveillance videos using the SSD algorithm
Husheng Pan,Yuzhen Li,Dezhu Zhao +2 more
TL;DR: Results demonstrate the SSD model-based recognition algorithm’s accuracy is significantly higher than that of Direct Part Marking and Fast Convolutional Neural Network algorithms and the detection efficiency is twice that of the R-CNN algorithm.
Journal ArticleDOI
Shear wave elastography characteristics of upper limb muscle in rigidity-dominant Parkinson's disease.
Chang Wei Ding,Xin Song,Xin Yu Fu,Ying Chun Zhang,Pan Mao,Yu Jing Sheng,Min Yang,Cai Shan Wang,Ying Zhang,Xiao Fang Chen,Cheng Jie Mao,Wei Feng Luo,Chun-Feng Liu +12 more
TL;DR: Differences in quantitative shear wave velocity (SWV) between patients with PD and normal controls were determined, indicating that SWE can be potentially used as an objective and quantitative tool for evaluating rigidity.
References
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