Research Article
Measles Rash Disease Classification Based on Various CNN Classifiers
@INPROCEEDINGS{10.1007/978-3-031-35078-8_2, author={Lohitha Rani Chintalapati and Trilok Sai Charan Tunuguntla and Yagnesh Challagundla and Sachi Nandan Mohanty and S. V. Sudha}, title={Measles Rash Disease Classification Based on Various CNN Classifiers}, proceedings={Intelligent Systems and Machine Learning. First EAI International Conference, ICISML 2022, Hyderabad, India, December 16-17, 2022, Proceedings, Part I}, proceedings_a={ICISML}, year={2023}, month={7}, keywords={Convolutional Neural Network Measles InceptionV3 SGD}, doi={10.1007/978-3-031-35078-8_2} }- Lohitha Rani Chintalapati
Trilok Sai Charan Tunuguntla
Yagnesh Challagundla
Sachi Nandan Mohanty
S. V. Sudha
Year: 2023
Measles Rash Disease Classification Based on Various CNN Classifiers
ICISML
Springer
DOI: 10.1007/978-3-031-35078-8_2
Abstract
One of the most thoroughly researched and well-documented non-linear infectious disease dynamical systems is measles. Infants and young children are most likely to contract the immunizable disease measles. Measles is a highly commutable viral infection that has a 90% secondary infection incidence among contacts who are vulnerable. In this study, we have used a deep convolutional neural network to discriminate between various skin diseases and measles rash. The categorization performance of each individually optimized DL model across all of their ensembles has been presented using the specified dataset. We tested four optimizers, namely SGD, ADAM, RMSprop, and RAdam, on three considered models in order to further improve them. These models include VGG16, InceptionV3, and ResNeXt50, on which individual 10-fold cross-validation is done. The maximum average 10-fold cross-validation accuracy of 98.62%, 99.31% recall, and 99.32% F1 score were achieved by the optimised Inception V3 using the SGD optimizer. Finally, our predictive model offers a method for early detection to assist physicians in treating and enforcing new laws and regulations.
