Impact of Hidden Dense Layers in Convolutional Neural Network to enhance Performance of Classification Model

Education and Health care sectors are two predominant areas where societal growth is expected through innovation and technology development. Machine Learning and Deep learning classification models have been entertained in predicting, detecting, and diagnosing major diseases in the early stage. In t...

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Bibliographic Details
Published in:IOP conference series. Materials Science and Engineering 2021-04, Vol.1131 (1), p.12007
Main Authors: Helen Josephine, V L, Nirmala, A.P., Alluri, Vijaya Lakshmi
Format: Article
Language:English
Subjects:
Artificial neural networks
Classification
Convolution
Impact analysis
Machine learning
Model testing
Neural networks
Performance enhancement
Scientific papers
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Summary:Education and Health care sectors are two predominant areas where societal growth is expected through innovation and technology development. Machine Learning and Deep learning classification models have been entertained in predicting, detecting, and diagnosing major diseases in the early stage. In this research paper, we have analyzed the impact of hidden dense layers in the Convolution neural network to improve the performance of the classification model. Three different classification deep network models have been constructed, analyzed and the result was tested with a diabetes dataset. Results concluded that the more layers with deeper the network better was the classification performance. The classification model with six hidden dense layers outperforms all other less number of hidden dense layers.
ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/1131/1/012007