Robust Medical X-Ray Image Classification by Deep Learning with Multi-Versus Optimizer
Classification of medical images plays an indispensable role in medical treatment and training tasks. Much effort and time are required in the extraction and selection of classification features of medical images. Deep Neural Networks (DNNs) are an evolving Machine Learning (ML) method that has prov...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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D. G. Pylarinos
2023-08-01
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| Series: | Engineering, Technology & Applied Science Research |
| Subjects: | |
| Online Access: | https://etasr.com/index.php/ETASR/article/view/6127 |
| Summary: | Classification of medical images plays an indispensable role in medical treatment and training tasks. Much effort and time are required in the extraction and selection of classification features of medical images. Deep Neural Networks (DNNs) are an evolving Machine Learning (ML) method that has proved its ability in various classification tasks. Convolutional Neural Networks (CNNs) present the optimal results for changing image classification tasks. In this regard, this study focused on developing a Multi-versus Optimizer with Deep Learning Enabled Robust Medical X-ray Image Classification (MVODL-RMXIC) method, aiming to identify abnormalities in medical X-ray images. The MVODL-RMXIC model used the Cross Bilateral Filtering (CBF) technique for noise removal, a MixNet feature extractor with an MVO algorithm based on hyperparameter optimization, and Bidirectional Long-Short-Term Memory (BiLSTM) for image classification. The proposed MVODL-RMXIC model was simulated and evaluated, showing its efficiency over other current methods.
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| ISSN: | 2241-4487 1792-8036 |