White blood cells classification using multi-fold pre-processing and optimized CNN model
Abstract White blood cells (WBCs) play a vital role in immune responses against infections and foreign agents. Different WBC types exist, and anomalies within them can indicate diseases like leukemia. Previous research suffers from limited accuracy and inflated performance due to the usage of less i...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-52880-0 |
| _version_ | 1797274592245448704 |
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| author | Oumaima Saidani Muhammad Umer Nazik Alturki Amal Alshardan Muniba Kiran Shtwai Alsubai Tai-Hoon Kim Imran Ashraf |
| author_facet | Oumaima Saidani Muhammad Umer Nazik Alturki Amal Alshardan Muniba Kiran Shtwai Alsubai Tai-Hoon Kim Imran Ashraf |
| author_sort | Oumaima Saidani |
| collection | DOAJ |
| description | Abstract White blood cells (WBCs) play a vital role in immune responses against infections and foreign agents. Different WBC types exist, and anomalies within them can indicate diseases like leukemia. Previous research suffers from limited accuracy and inflated performance due to the usage of less important features. Moreover, these studies often focus on fewer WBC types, exaggerating accuracy. This study addresses the crucial task of classifying WBC types using microscopic images. This study introduces a novel approach using extensive pre-processing with data augmentation techniques to produce a more significant feature set to achieve more promising results. The study conducts experiments employing both conventional deep learning and transfer learning models, comparing performance with state-of-the-art machine and deep learning models. Results reveal that a pre-processed feature set and convolutional neural network classifier achieves a significantly better accuracy of 0.99. The proposed method demonstrates superior accuracy and computational efficiency compared to existing state-of-the-art works. |
| first_indexed | 2024-03-07T15:00:33Z |
| format | Article |
| id | doaj.art-926d2866d2f641208643d21487e7faf1 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:00:33Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-926d2866d2f641208643d21487e7faf12024-03-05T19:09:18ZengNature PortfolioScientific Reports2045-23222024-02-0114111410.1038/s41598-024-52880-0White blood cells classification using multi-fold pre-processing and optimized CNN modelOumaima Saidani0Muhammad Umer1Nazik Alturki2Amal Alshardan3Muniba Kiran4Shtwai Alsubai5Tai-Hoon Kim6Imran Ashraf7Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman UniversityDepartment of Computer Science and Information Technology, The Islamia University of BahawalpurDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman UniversityDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman UniversityDepartment of Biotechnology, Virtual University of PakistanDepartment of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz UniversitySchool of Electrical and Computer Engineering, Yeosu Campus, Chonnam National UniversityInformation and Communication Engineering, Yeungnam UniversityAbstract White blood cells (WBCs) play a vital role in immune responses against infections and foreign agents. Different WBC types exist, and anomalies within them can indicate diseases like leukemia. Previous research suffers from limited accuracy and inflated performance due to the usage of less important features. Moreover, these studies often focus on fewer WBC types, exaggerating accuracy. This study addresses the crucial task of classifying WBC types using microscopic images. This study introduces a novel approach using extensive pre-processing with data augmentation techniques to produce a more significant feature set to achieve more promising results. The study conducts experiments employing both conventional deep learning and transfer learning models, comparing performance with state-of-the-art machine and deep learning models. Results reveal that a pre-processed feature set and convolutional neural network classifier achieves a significantly better accuracy of 0.99. The proposed method demonstrates superior accuracy and computational efficiency compared to existing state-of-the-art works.https://doi.org/10.1038/s41598-024-52880-0 |
| spellingShingle | Oumaima Saidani Muhammad Umer Nazik Alturki Amal Alshardan Muniba Kiran Shtwai Alsubai Tai-Hoon Kim Imran Ashraf White blood cells classification using multi-fold pre-processing and optimized CNN model Scientific Reports |
| title | White blood cells classification using multi-fold pre-processing and optimized CNN model |
| title_full | White blood cells classification using multi-fold pre-processing and optimized CNN model |
| title_fullStr | White blood cells classification using multi-fold pre-processing and optimized CNN model |
| title_full_unstemmed | White blood cells classification using multi-fold pre-processing and optimized CNN model |
| title_short | White blood cells classification using multi-fold pre-processing and optimized CNN model |
| title_sort | white blood cells classification using multi fold pre processing and optimized cnn model |
| url | https://doi.org/10.1038/s41598-024-52880-0 |
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