A hybrid algorithm for lung cancer classification using SVM and Neural Networks
| Autor: | Paramjit Singh, Pankaj Nanglia, Aparna N. Mahajan, Sumit Kumar, Davinder Rathee |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Kernel attribute selected classifier
Computer Networks and Communications Computer science Information technology 02 engineering and technology Back propagation neural network Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Research article Block-architecture Artificial neural network business.industry 020208 electrical & electronic engineering 020206 networking & telecommunications Pattern recognition T58.5-58.64 Support vector machine ComputingMethodologies_PATTERNRECOGNITION Hardware and Architecture Hybrid classification Computation complexity Artificial intelligence Lung cancer business Classifier (UML) Software Information Systems |
| Zdroj: | ICT Express, Vol 7, Iss 3, Pp 335-341 (2021) |
| ISSN: | 2405-9595 |
| DOI: | 10.1016/j.icte.2020.06.007 |
| Popis: | The present research article focused on the factual findings of the potential usage of the combinational Feed-Forward Back Propagation Neural Network as a judgment making for lung cancer. In this context, Support Vector Machine is integrated with Feed-Forward Back Propagation Neural Network to create a hybrid algorithm that further helps in reducing the computation complexity of the classification. A set of 500 images are utilized in which 75% data is used for the training purpose and the rest 25% is used to achieve the classification. In the view of forgoing, a three-block mechanism is proposed for the classification in which the first block preprocesses the dataset, the second block extracts the features via the SURF technique followed by the optimization using Genetic Algorithm and the terminal block is for the classification via FFBPNN. The hybrid classification algorithm is named as Kernel Attribute Selected Classifier and the overall classification accuracy of the proposed algorithm is 98.08%. Herein, the objective of the study is to enhance the classification accuracy by applying a hybrid classification algorithm. |
| Databáze: | OpenAIRE |
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