Explained variance ration of the PCA algorithm.
<div><p>Chest X-ray image classification plays an important role in medical diagnostics. Machine learning algorithms enhanced the performance of these classification algorithms by introducing advance techniques. These classification algorithms often requires conversion of a medical data...
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2025
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| _version_ | 1852019412098351104 |
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| author | Abeer Aljohani (18497914) |
| author_facet | Abeer Aljohani (18497914) |
| author_role | author |
| dc.creator.none.fl_str_mv | Abeer Aljohani (18497914) |
| dc.date.none.fl_str_mv | 2025-06-11T17:28:38Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0325058.g013 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Explained_variance_ration_of_the_PCA_algorithm_/29294963 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biotechnology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified three different classes support vector machine provides satisfactory results introducing advance techniques image pre processing calculate spectral moment legendre polynomails space random forest improves random forest algorithm latest data set base set composed improved chest x given medical image spectral moments generation random forest data set spectral space ray image medical data given x chest x spectral moments spectral coefficients medical diagnostics another space original data special functions ray images proposed method proposed approach orthogonal system moments designed mathematical theory important values important role fast version covid infected computation softwares classification task classification algorithms |
| dc.title.none.fl_str_mv | Explained variance ration of the PCA algorithm. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>Chest X-ray image classification plays an important role in medical diagnostics. Machine learning algorithms enhanced the performance of these classification algorithms by introducing advance techniques. These classification algorithms often requires conversion of a medical data to another space in which the original data is reduced to important values or moments. We developed a mechanism which converts a given medical image to a spectral space which have a base set composed of special functions. In this study, we propose a chest X-ray image classification method based on spectral coefficients. The spectral coefficients are based on an orthogonal system of Legendre type smooth polynomials. We developed the mathematical theory to calculate spectral moment in Legendre polynomails space and use these moments to train traditional classifier like SVM and random forest for a classification task. The procedure is applied to a latest data set of X-Ray images. The data set is composed of X-Ray images of three different classes of patients, normal, Covid infected and pneumonia. The moments designed in this study, when used in SVM or random forest improves its ability to classify a given X-Ray image at a high accuracy. A parametric study of the proposed approach is presented. The performance of these spectral moments is checked in Support vector machine and Random forest algorithm. The efficiency and accuracy of the proposed method is presented in details. All our simulation is performed in computation softwares, Matlab and Python. The image pre processing and spectral moments generation is performed in Matlab and the implementation of the classifiers is performed with python. It is observed that the proposed approach works well and provides satisfactory results (0.975 accuracy), however further studies are required to establish a more accurate and fast version of this approach.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_ac3eba56f6f0dd83700be1d356a1b4e2 |
| identifier_str_mv | 10.1371/journal.pone.0325058.g013 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29294963 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Explained variance ration of the PCA algorithm.Abeer Aljohani (18497914)BiotechnologySpace ScienceEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedthree different classessupport vector machineprovides satisfactory resultsintroducing advance techniquesimage pre processingcalculate spectral momentlegendre polynomails spacerandom forest improvesrandom forest algorithmlatest data setbase set composedimproved chest xgiven medical imagespectral moments generationrandom forestdata setspectral spaceray imagemedical datagiven xchest xspectral momentsspectral coefficientsmedical diagnosticsanother spaceoriginal dataspecial functionsray imagesproposed methodproposed approachorthogonal systemmoments designedmathematical theoryimportant valuesimportant rolefast versioncovid infectedcomputation softwaresclassification taskclassification algorithms<div><p>Chest X-ray image classification plays an important role in medical diagnostics. Machine learning algorithms enhanced the performance of these classification algorithms by introducing advance techniques. These classification algorithms often requires conversion of a medical data to another space in which the original data is reduced to important values or moments. We developed a mechanism which converts a given medical image to a spectral space which have a base set composed of special functions. In this study, we propose a chest X-ray image classification method based on spectral coefficients. The spectral coefficients are based on an orthogonal system of Legendre type smooth polynomials. We developed the mathematical theory to calculate spectral moment in Legendre polynomails space and use these moments to train traditional classifier like SVM and random forest for a classification task. The procedure is applied to a latest data set of X-Ray images. The data set is composed of X-Ray images of three different classes of patients, normal, Covid infected and pneumonia. The moments designed in this study, when used in SVM or random forest improves its ability to classify a given X-Ray image at a high accuracy. A parametric study of the proposed approach is presented. The performance of these spectral moments is checked in Support vector machine and Random forest algorithm. The efficiency and accuracy of the proposed method is presented in details. All our simulation is performed in computation softwares, Matlab and Python. The image pre processing and spectral moments generation is performed in Matlab and the implementation of the classifiers is performed with python. It is observed that the proposed approach works well and provides satisfactory results (0.975 accuracy), however further studies are required to establish a more accurate and fast version of this approach.</p></div>2025-06-11T17:28:38ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0325058.g013https://figshare.com/articles/figure/Explained_variance_ration_of_the_PCA_algorithm_/29294963CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/292949632025-06-11T17:28:38Z |
| spellingShingle | Explained variance ration of the PCA algorithm. Abeer Aljohani (18497914) Biotechnology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified three different classes support vector machine provides satisfactory results introducing advance techniques image pre processing calculate spectral moment legendre polynomails space random forest improves random forest algorithm latest data set base set composed improved chest x given medical image spectral moments generation random forest data set spectral space ray image medical data given x chest x spectral moments spectral coefficients medical diagnostics another space original data special functions ray images proposed method proposed approach orthogonal system moments designed mathematical theory important values important role fast version covid infected computation softwares classification task classification algorithms |
| status_str | publishedVersion |
| title | Explained variance ration of the PCA algorithm. |
| title_full | Explained variance ration of the PCA algorithm. |
| title_fullStr | Explained variance ration of the PCA algorithm. |
| title_full_unstemmed | Explained variance ration of the PCA algorithm. |
| title_short | Explained variance ration of the PCA algorithm. |
| title_sort | Explained variance ration of the PCA algorithm. |
| topic | Biotechnology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified three different classes support vector machine provides satisfactory results introducing advance techniques image pre processing calculate spectral moment legendre polynomails space random forest improves random forest algorithm latest data set base set composed improved chest x given medical image spectral moments generation random forest data set spectral space ray image medical data given x chest x spectral moments spectral coefficients medical diagnostics another space original data special functions ray images proposed method proposed approach orthogonal system moments designed mathematical theory important values important role fast version covid infected computation softwares classification task classification algorithms |