scholarly journals Multi-Focus Image Fusion Based on Convolution Neural Network for Parkinson’s Disease Image Classification

Diagnostics ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2379
Author(s):  
Yin Dai ◽  
Yumeng Song ◽  
Weibin Liu ◽  
Wenhe Bai ◽  
Yifan Gao ◽  
...  

Parkinson’s disease (PD) is a common neurodegenerative disease that has a significant impact on people’s lives. Early diagnosis is imperative since proper treatment stops the disease’s progression. With the rapid development of CAD techniques, there have been numerous applications of computer-aided diagnostic (CAD) techniques in the diagnosis of PD. In recent years, image fusion has been applied in various fields and is valuable in medical diagnosis. This paper mainly adopts a multi-focus image fusion method primarily based on deep convolutional neural networks to fuse magnetic resonance images (MRI) and positron emission tomography (PET) neural photographs into multi-modal images. Additionally, the study selected Alexnet, Densenet, ResNeSt, and Efficientnet neural networks to classify the single-modal MRI dataset and the multi-modal dataset. The test accuracy rates of the single-modal MRI dataset are 83.31%, 87.76%, 86.37%, and 86.44% on the Alexnet, Densenet, ResNeSt, and Efficientnet, respectively. Moreover, the test accuracy rates of the multi-modal fusion dataset on the Alexnet, Densenet, ResNeSt, and Efficientnet are 90.52%, 97.19%, 94.15%, and 93.39%. As per all four networks discussed above, it can be concluded that the test results for the multi-modal dataset are better than those for the single-modal MRI dataset. The experimental results showed that the multi-focus image fusion method according to deep learning can enhance the accuracy of PD image classification.

2021 ◽  
Vol 92 ◽  
pp. 107174
Author(s):  
Yang Zhou ◽  
Xiaomin Yang ◽  
Rongzhu Zhang ◽  
Kai Liu ◽  
Marco Anisetti ◽  
...  

2020 ◽  
Vol 176 ◽  
pp. 107681
Author(s):  
Di Gai ◽  
Xuanjing Shen ◽  
Haipeng Chen ◽  
Pengxiang Su

Optik ◽  
2015 ◽  
Vol 126 (20) ◽  
pp. 2508-2511 ◽  
Author(s):  
Jingjing Wang ◽  
Qian Li ◽  
Zhenhong Jia ◽  
Nikola Kasabov ◽  
Jie Yang

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 16 ◽  
Author(s):  
Lucijano Berus ◽  
Simon Klancnik ◽  
Miran Brezocnik ◽  
Mirko Ficko

In recent years, neural networks have become very popular in all kinds of prediction problems. In this paper, multiple feed-forward artificial neural networks (ANNs) with various configurations are used in the prediction of Parkinson’s disease (PD) of tested individuals, based on extracted features from 26 different voice samples per individual. Results are validated via the leave-one-subject-out (LOSO) scheme. Few feature selection procedures based on Pearson’s correlation coefficient, Kendall’s correlation coefficient, principal component analysis, and self-organizing maps, have been used for boosting the performance of algorithms and for data reduction. The best test accuracy result has been achieved with Kendall’s correlation coefficient-based feature selection, and the most relevant voice samples are recognized. Multiple ANNs have proven to be the best classification technique for diagnosis of PD without usage of the feature selection procedure (on raw data). Finally, a neural network is fine-tuned, and a test accuracy of 86.47% was achieved.


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