Blood cancer prediction model based on deep learning technique
Abstract Blood cancer is among the critical health concerns among people around the world and normally emanates from genetic and environmental issues. Early detection becomes essential, as the rate of death associated with it is high, to ensure that the rate of treatment success is up, and mortality...
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| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-84475-0 |
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| author | Amr I. Shehta Mona Nasr Alaa El Din M. El Ghazali |
| author_facet | Amr I. Shehta Mona Nasr Alaa El Din M. El Ghazali |
| author_sort | Amr I. Shehta |
| collection | DOAJ |
| description | Abstract Blood cancer is among the critical health concerns among people around the world and normally emanates from genetic and environmental issues. Early detection becomes essential, as the rate of death associated with it is high, to ensure that the rate of treatment success is up, and mortality reduced. This paper focuses on improving blood cancer diagnosis using advanced deep learning techniques like ResNetRS50, RegNetX016, AlexNet, Convnext, EfficientNet, Inception_V3, Xception, and VGG19. Among the models assessed, ResNetRS50 had better accuracy and speed with minimal error rates compared with other state-of-the-arts. This work will exploit the power of ResNetRS50 in contributing to early detection and reducing bad outcomes for blood cancer patients. Blood cancer is currently one of the deadliest diseases worldwide, resulting from a combination of genetic and non-genetic factors. It stands as a leading cause of cancer-related deaths in both developed and developing nations. Early detection of cancer is pivotal in reducing mortality rates, as it increases the likelihood of successful treatment and potential cure. The objective is to decrease mortality rates through early diagnosis of blood cancer, thus offering individuals a better chance of survival from this disease. |
| format | Article |
| id | doaj-art-bcaa50a7006b4385bd0d473d70d96273 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-bcaa50a7006b4385bd0d473d70d962732025-01-19T12:20:01ZengNature PortfolioScientific Reports2045-23222025-01-0115111610.1038/s41598-024-84475-0Blood cancer prediction model based on deep learning techniqueAmr I. Shehta0Mona Nasr1Alaa El Din M. El Ghazali2Department of Information System, Faculty of Computers and Artificial Intelligence, Helwan UniversityDepartment of Information System, Faculty of Computers and Artificial Intelligence, Helwan UniversityDepartment of Computer and Information Systems, Sadat Academy for Management SciencesAbstract Blood cancer is among the critical health concerns among people around the world and normally emanates from genetic and environmental issues. Early detection becomes essential, as the rate of death associated with it is high, to ensure that the rate of treatment success is up, and mortality reduced. This paper focuses on improving blood cancer diagnosis using advanced deep learning techniques like ResNetRS50, RegNetX016, AlexNet, Convnext, EfficientNet, Inception_V3, Xception, and VGG19. Among the models assessed, ResNetRS50 had better accuracy and speed with minimal error rates compared with other state-of-the-arts. This work will exploit the power of ResNetRS50 in contributing to early detection and reducing bad outcomes for blood cancer patients. Blood cancer is currently one of the deadliest diseases worldwide, resulting from a combination of genetic and non-genetic factors. It stands as a leading cause of cancer-related deaths in both developed and developing nations. Early detection of cancer is pivotal in reducing mortality rates, as it increases the likelihood of successful treatment and potential cure. The objective is to decrease mortality rates through early diagnosis of blood cancer, thus offering individuals a better chance of survival from this disease.https://doi.org/10.1038/s41598-024-84475-0Blood cancerMedical deep learningClassificationResNetRS50RegNetX016VGG19 |
| spellingShingle | Amr I. Shehta Mona Nasr Alaa El Din M. El Ghazali Blood cancer prediction model based on deep learning technique Scientific Reports Blood cancer Medical deep learning Classification ResNetRS50 RegNetX016 VGG19 |
| title | Blood cancer prediction model based on deep learning technique |
| title_full | Blood cancer prediction model based on deep learning technique |
| title_fullStr | Blood cancer prediction model based on deep learning technique |
| title_full_unstemmed | Blood cancer prediction model based on deep learning technique |
| title_short | Blood cancer prediction model based on deep learning technique |
| title_sort | blood cancer prediction model based on deep learning technique |
| topic | Blood cancer Medical deep learning Classification ResNetRS50 RegNetX016 VGG19 |
| url | https://doi.org/10.1038/s41598-024-84475-0 |
| work_keys_str_mv | AT amrishehta bloodcancerpredictionmodelbasedondeeplearningtechnique AT monanasr bloodcancerpredictionmodelbasedondeeplearningtechnique AT alaaeldinmelghazali bloodcancerpredictionmodelbasedondeeplearningtechnique |