An Intelligent Medical Imaging Approach for Various Blood Structure Classifications
Blood is a vital body fluid and can be instrumental in identifying various pathological conditions. Nowadays, a lot of people are suffering from COVID-19 and every country has its own limited testing capacity. Consequently, a system is required to help doctors analyze a patient’s blood structure inc...
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/5573300 |
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| author | Madallah Alruwaili |
| author_facet | Madallah Alruwaili |
| author_sort | Madallah Alruwaili |
| collection | DOAJ |
| description | Blood is a vital body fluid and can be instrumental in identifying various pathological conditions. Nowadays, a lot of people are suffering from COVID-19 and every country has its own limited testing capacity. Consequently, a system is required to help doctors analyze a patient’s blood structure including COVID-19. Therefore, in this paper, we extracted and selected blood features by proposing a new feature extraction and selection method named stepwise linear discriminant analysis (SWLDA). SWLDA emphasizes on picking confined features from blood structure images and discerning its class based on reversion value such as partial F value. SWLDA begins with picking an equivalence comprising the sole finest X variable and then puts in effort to add more Xs individually, providing the situations are adequate. The process of adding and picking is based on F value to determine which variable would be entered. Then, the picked or the default F-to-enter value is compared with the uppermost partial F value. After this step, the forward addition or backward removal begins and whether the partial test values for all the predictor variables already in the line are estimated is known. Then, the comparison is made between the lowermost partial test value (FL) and preselected or defaulting consequence levels such as F0 (i.e., if F0 > FL, the variable ZL is removed, and the F test is started again; otherwise, the regression equation is adopted). Finally, the system is trained by employing support vector machine (SVM) to label the blood images. The performance of the proposed approach is assessed by employing 8 different datasets of blood structures. It is assured that the proposed method has achieved significant results under different blood structure images including COVID-19. |
| format | Article |
| id | doaj-art-9a9116e37bf5459f80073f700f898cd6 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-9a9116e37bf5459f80073f700f898cd62025-02-03T06:43:55ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/55733005573300An Intelligent Medical Imaging Approach for Various Blood Structure ClassificationsMadallah Alruwaili0Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka, Saudi ArabiaBlood is a vital body fluid and can be instrumental in identifying various pathological conditions. Nowadays, a lot of people are suffering from COVID-19 and every country has its own limited testing capacity. Consequently, a system is required to help doctors analyze a patient’s blood structure including COVID-19. Therefore, in this paper, we extracted and selected blood features by proposing a new feature extraction and selection method named stepwise linear discriminant analysis (SWLDA). SWLDA emphasizes on picking confined features from blood structure images and discerning its class based on reversion value such as partial F value. SWLDA begins with picking an equivalence comprising the sole finest X variable and then puts in effort to add more Xs individually, providing the situations are adequate. The process of adding and picking is based on F value to determine which variable would be entered. Then, the picked or the default F-to-enter value is compared with the uppermost partial F value. After this step, the forward addition or backward removal begins and whether the partial test values for all the predictor variables already in the line are estimated is known. Then, the comparison is made between the lowermost partial test value (FL) and preselected or defaulting consequence levels such as F0 (i.e., if F0 > FL, the variable ZL is removed, and the F test is started again; otherwise, the regression equation is adopted). Finally, the system is trained by employing support vector machine (SVM) to label the blood images. The performance of the proposed approach is assessed by employing 8 different datasets of blood structures. It is assured that the proposed method has achieved significant results under different blood structure images including COVID-19.http://dx.doi.org/10.1155/2021/5573300 |
| spellingShingle | Madallah Alruwaili An Intelligent Medical Imaging Approach for Various Blood Structure Classifications Complexity |
| title | An Intelligent Medical Imaging Approach for Various Blood Structure Classifications |
| title_full | An Intelligent Medical Imaging Approach for Various Blood Structure Classifications |
| title_fullStr | An Intelligent Medical Imaging Approach for Various Blood Structure Classifications |
| title_full_unstemmed | An Intelligent Medical Imaging Approach for Various Blood Structure Classifications |
| title_short | An Intelligent Medical Imaging Approach for Various Blood Structure Classifications |
| title_sort | intelligent medical imaging approach for various blood structure classifications |
| url | http://dx.doi.org/10.1155/2021/5573300 |
| work_keys_str_mv | AT madallahalruwaili anintelligentmedicalimagingapproachforvariousbloodstructureclassifications AT madallahalruwaili intelligentmedicalimagingapproachforvariousbloodstructureclassifications |