A Novel Hybrid Machine Learning-Based System Using Deep Learning Techniques and Meta-Heuristic Algorithms for Various Medical Datatypes Classification Article Swipe
Yezi Ali Kadhim
,
Mehmet Serdar Güzel
,
Alok Mishra
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics14141469
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics14141469
Medicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep learning techniques, which were based on a convolutional neural network (CNN) or autoencoder, to extract features and combine them with the next step of the meta-heuristic algorithm in order to select optimal features using the particle swarm optimization (PSO) algorithm. This combination sought to reduce the dimensionality of the datasets while maintaining the original performance of the data. This is considered an innovative method and ensures highly accurate classification results across various medical datasets. Several classifiers were employed to predict the diseases. The COVID-19 dataset found that the highest accuracy was 99.76% using the combination of CNN-PSO-SVM. In comparison, the brain tumor dataset obtained 99.51% accuracy, the highest accuracy derived using the combination method of autoencoder-PSO-KNN.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/diagnostics14141469
- OA Status
- gold
- Cited By
- 9
- References
- 86
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4400453937Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/diagnostics14141469Digital Object Identifier
- Title
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A Novel Hybrid Machine Learning-Based System Using Deep Learning Techniques and Meta-Heuristic Algorithms for Various Medical Datatypes ClassificationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
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2024-07-09Full publication date if available
- Authors
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Yezi Ali Kadhim, Mehmet Serdar Güzel, Alok MishraList of authors in order
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https://doi.org/10.3390/diagnostics14141469Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.3390/diagnostics14141469Direct OA link when available
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Artificial intelligence, Computer science, Autoencoder, Machine learning, Particle swarm optimization, Deep learning, Algorithm, Convolutional neural network, Support vector machine, Heuristic, Artificial neural network, Curse of dimensionality, Pattern recognition (psychology), Data miningTop concepts (fields/topics) attached by OpenAlex
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9Total citation count in OpenAlex
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2025: 7, 2024: 2Per-year citation counts (last 5 years)
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| abstract_inverted_index.99.76% | 199 |
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