Predictive analysis of brain imaging data based on deep learning algorithms
This paper analyzes the effectiveness of predictive analysis of brain imaging data based on deep learning algorithms, and improves the prediction accuracy and efficiency of brain imaging data through improved methods. The first step is to measure the local consistency of the brain imaging data using...
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| Format: | Article |
| Language: | English |
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Sciendo
2024-01-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
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| Online Access: | https://doi.org/10.2478/amns-2024-0702 |
| _version_ | 1797229604689149952 |
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| author | Wang Xuan Zhang Xiaotong Zhang Yuchen |
| author_facet | Wang Xuan Zhang Xiaotong Zhang Yuchen |
| author_sort | Wang Xuan |
| collection | DOAJ |
| description | This paper analyzes the effectiveness of predictive analysis of brain imaging data based on deep learning algorithms, and improves the prediction accuracy and efficiency of brain imaging data through improved methods. The first step is to measure the local consistency of the brain imaging data using Kendall’s concordance coefficient (KCC), and to analyze the differences between the datasets using the two-sample t-test. Secondly, a batch normalized convolutional neural network (BN-CNN)-based prediction method for brain imaging data has been developed. This method extracts spatial and temporal features in two convolutional layers, followed by a fully connected layer for classification. Experimental results show that this method is helpful in predicting missing structural data in brain imaging. Secondly, a batch normalized convolutional neural network (BN-CNN) based brain imaging data prediction method is developed, which extracts spatial and temporal features in two convolutional layers. Then it connects to a fully connected layer for classification. The experimental results show that this method’s structural similarity index (SSIM) and feature similarity index (FSIM) in brain imaging data prediction of missing data reaches 0.9446 and 0.9465, respectively, which is significantly better than that of other GAN benchmarks. In applying the method to epilepsy and Parkinson’s cases, this algorithm is used to epilepsy and Parkinson’s cases, and a two-sample t-test analyzes the differences in the data sets. In the application of epilepsy and Parkinson’s cases, the algorithm in this paper has an average prediction accuracy of 93.37%, effectively reducing the rate of incorrect predictions. Deep learning algorithms are highly efficient and accurate in predicting brain imaging data, which is crucial for future clinical diagnosis and treatment. |
| first_indexed | 2024-04-24T15:15:14Z |
| format | Article |
| id | doaj.art-b20e1a465f16449fb6eadeb727e8acc7 |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-04-24T15:15:14Z |
| publishDate | 2024-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-b20e1a465f16449fb6eadeb727e8acc72024-04-02T09:28:42ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562024-01-019110.2478/amns-2024-0702Predictive analysis of brain imaging data based on deep learning algorithmsWang Xuan0Zhang Xiaotong1Zhang Yuchen21School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China.1School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China.2School of Electronic and Information Engineering, Tongji University, Shanghai, 201804, China.This paper analyzes the effectiveness of predictive analysis of brain imaging data based on deep learning algorithms, and improves the prediction accuracy and efficiency of brain imaging data through improved methods. The first step is to measure the local consistency of the brain imaging data using Kendall’s concordance coefficient (KCC), and to analyze the differences between the datasets using the two-sample t-test. Secondly, a batch normalized convolutional neural network (BN-CNN)-based prediction method for brain imaging data has been developed. This method extracts spatial and temporal features in two convolutional layers, followed by a fully connected layer for classification. Experimental results show that this method is helpful in predicting missing structural data in brain imaging. Secondly, a batch normalized convolutional neural network (BN-CNN) based brain imaging data prediction method is developed, which extracts spatial and temporal features in two convolutional layers. Then it connects to a fully connected layer for classification. The experimental results show that this method’s structural similarity index (SSIM) and feature similarity index (FSIM) in brain imaging data prediction of missing data reaches 0.9446 and 0.9465, respectively, which is significantly better than that of other GAN benchmarks. In applying the method to epilepsy and Parkinson’s cases, this algorithm is used to epilepsy and Parkinson’s cases, and a two-sample t-test analyzes the differences in the data sets. In the application of epilepsy and Parkinson’s cases, the algorithm in this paper has an average prediction accuracy of 93.37%, effectively reducing the rate of incorrect predictions. Deep learning algorithms are highly efficient and accurate in predicting brain imaging data, which is crucial for future clinical diagnosis and treatment.https://doi.org/10.2478/amns-2024-0702brain imaging datadeep learningkendall’s harmony coefficientdata prediction65y04 |
| spellingShingle | Wang Xuan Zhang Xiaotong Zhang Yuchen Predictive analysis of brain imaging data based on deep learning algorithms Applied Mathematics and Nonlinear Sciences brain imaging data deep learning kendall’s harmony coefficient data prediction 65y04 |
| title | Predictive analysis of brain imaging data based on deep learning algorithms |
| title_full | Predictive analysis of brain imaging data based on deep learning algorithms |
| title_fullStr | Predictive analysis of brain imaging data based on deep learning algorithms |
| title_full_unstemmed | Predictive analysis of brain imaging data based on deep learning algorithms |
| title_short | Predictive analysis of brain imaging data based on deep learning algorithms |
| title_sort | predictive analysis of brain imaging data based on deep learning algorithms |
| topic | brain imaging data deep learning kendall’s harmony coefficient data prediction 65y04 |
| url | https://doi.org/10.2478/amns-2024-0702 |
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