A Novel Hybrid Machine Learning-Based System Using Deep Learning Techniques and Meta-Heuristic Algorithms for Various Medical Datatypes Classification
dc.authorid | Mishra, Alok/0000-0003-1275-2050 | |
dc.authorscopusid | 57214819564 | |
dc.authorscopusid | 36349844700 | |
dc.authorscopusid | 7201441575 | |
dc.authorwosid | Mishra, Alok/D-7937-2012 | |
dc.contributor.author | Mıshra, Alok | |
dc.contributor.author | Guzel, Mehmet Serdar | |
dc.contributor.author | Mishra, Alok | |
dc.contributor.other | Software Engineering | |
dc.date.accessioned | 2024-09-10T21:32:54Z | |
dc.date.available | 2024-09-10T21:32:54Z | |
dc.date.issued | 2024 | |
dc.department | Atılım University | en_US |
dc.department-temp | [Kadhim, Yezi Ali] Univ Baghdad, Coll Engn, Jadriyah 10071, Baghdad, Iraq; [Kadhim, Yezi Ali] Atilim Univ, Dept Modeling & Design Engn Syst MODES, TR-06830 Ankara, Turkiye; [Kadhim, Yezi Ali] Atilim Univ, Dept Elect & Elect Engn, TR-06830 Incek, Ankara, Turkiye; [Guzel, Mehmet Serdar] Ankara Univ, Dept Comp Engn, TR-06100 Yenimahalle, Ankara, Turkiye; [Mishra, Alok] Norwegian Univ Sci & Technol, Fac Engn Sci & Technol, N-7034 Trondheim, Norway; [Mishra, Alok] Atilim Univ, Dept Software Engn, TR-06830 Incek, Ankara, Turkiye | en_US |
dc.description | Mishra, Alok/0000-0003-1275-2050 | en_US |
dc.description.abstract | 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. | en_US |
dc.description.sponsorship | Norwegian University of Science and Technology | en_US |
dc.description.sponsorship | The authors thank the Norwegian University of Science and Technology, for the support of Open access fund. | en_US |
dc.description.woscitationindex | Science Citation Index Expanded | |
dc.identifier.citation | 0 | |
dc.identifier.doi | 10.3390/diagnostics14141469 | |
dc.identifier.issn | 2075-4418 | |
dc.identifier.issue | 14 | en_US |
dc.identifier.pmid | 39061605 | |
dc.identifier.scopus | 2-s2.0-85199581111 | |
dc.identifier.scopusquality | Q2 | |
dc.identifier.uri | https://doi.org/10.3390/diagnostics14141469 | |
dc.identifier.uri | https://hdl.handle.net/20.500.14411/7275 | |
dc.identifier.volume | 14 | en_US |
dc.identifier.wos | WOS:001276739900001 | |
dc.identifier.wosquality | Q2 | |
dc.language.iso | en | en_US |
dc.publisher | Mdpi | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | deep learning | en_US |
dc.subject | autoencoder | en_US |
dc.subject | classification | en_US |
dc.subject | medical dataset | en_US |
dc.subject | COVID-19 | en_US |
dc.subject | brain tumor | en_US |
dc.subject | meta-heuristic algorithm | en_US |
dc.title | A Novel Hybrid Machine Learning-Based System Using Deep Learning Techniques and Meta-Heuristic Algorithms for Various Medical Datatypes Classification | en_US |
dc.type | Article | en_US |
dspace.entity.type | Publication | |
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