A novel solution of using deep learning for left ventricle detection: Enhanced feature extraction
| Autor: | Duong Thu Hang Pham, Kiran Sharma, Abeer Alsadoon, P. W. C. Prasad, Thair Al-Dala'in, Tran Quoc Vinh Nguyen |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
Heart Ventricles Feature extraction Health Informatics Overfitting Convolutional neural network 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Deep Learning medicine Dropout (neural networks) Vanishing gradient problem business.industry Deep learning Pattern recognition Computer Science Applications medicine.anatomical_structure Ventricle Artificial intelligence Neural Networks Computer Gradient descent business 030217 neurology & neurosurgery Software Algorithms |
| Zdroj: | Computer methods and programs in biomedicine. 197 |
| ISSN: | 1872-7565 |
| Popis: | Background and aim deep learning algorithms have not been successfully used for the left ventricle (LV) detection in echocardiographic images due to overfitting and vanishing gradient descent problem. This research aims to increase accuracy and improves the processing time of the left ventricle detection process by reducing the overfitting and vanishing gradient problem. Methodology the proposed system consists of an enhanced deep convolutional neural network with an extra convolutional layer, and dropout layer to solve the problem of overfitting and vanishing gradient. Data augmentation was used for increasing the accuracy of feature extraction for left ventricle detection. Results four pathological groups of datasets were used for training and evaluation of the model: heart failure without infarction, heart failure with infarction, and hypertrophy, and healthy. The proposed model provided an accuracy of 94% in left ventricle detection for all the groups compared to the other current systems. The results showed that the processing time was reduced from 0.45 s to 0.34 s in an average. Conclusion the proposed system enhances accuracy and decreases processing time in the left ventricle detection. This paper solves the issues of overfitting of the data. |
| Databáze: | OpenAIRE |
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