Detection of sickle cell disease using deep neural networks and explainable artificial intelligence
Sickle cell disease (SCD), a blood disorder that transforms the shape of red blood cells into a distinctive sickle form, is a major concern as it not only compromises the blood’s oxygen-carrying capacity but also poses significant health risks, ranging from weakness to paralysis and, in severe cases...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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De Gruyter
2024-04-01
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| Series: | Journal of Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jisys-2023-0179 |
| _version_ | 1797208498631606272 |
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| author | Goswami Neelankit Gautam Goswami Anushree Sampathila Niranjana Bairy Muralidhar G. Chadaga Krishnaraj Belurkar Sushma |
| author_facet | Goswami Neelankit Gautam Goswami Anushree Sampathila Niranjana Bairy Muralidhar G. Chadaga Krishnaraj Belurkar Sushma |
| author_sort | Goswami Neelankit Gautam |
| collection | DOAJ |
| description | Sickle cell disease (SCD), a blood disorder that transforms the shape of red blood cells into a distinctive sickle form, is a major concern as it not only compromises the blood’s oxygen-carrying capacity but also poses significant health risks, ranging from weakness to paralysis and, in severe cases, even fatality. This condition not only underscores the pressing need for innovative solutions but also encapsulates the broader challenges faced by medical professionals, including delayed treatment, protracted processes, and the potential for subjective errors in diagnosis and classification. Consequently, the application of artificial intelligence (AI) in healthcare has emerged as a transformative force, inspiring multidisciplinary efforts to overcome the complexities associated with SCD and enhance diagnostic accuracy and treatment outcomes. The use of transfer learning helps to extract features from the input dataset and give an accurate prediction. We analyse and compare the performance parameters of three distinct models for this purpose: GoogLeNet, ResNet18, and ResNet50. The best results were shown by the ResNet50 model, with an accuracy of 94.90%. Explainable AI is the best approach for transparency and confirmation of the predictions made by the classifiers. This research utilizes Grad-CAM to interpret and make the models more reliable. Therefore, this specific approach benefits pathologists through its speed, precision, and accuracy of classification of sickle cells. |
| first_indexed | 2024-04-24T09:39:46Z |
| format | Article |
| id | doaj.art-59f7a7c257ed4ce5a3b15784c98c4703 |
| institution | Directory Open Access Journal |
| issn | 2191-026X |
| language | English |
| last_indexed | 2024-04-24T09:39:46Z |
| publishDate | 2024-04-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Intelligent Systems |
| spelling | doaj.art-59f7a7c257ed4ce5a3b15784c98c47032024-04-15T07:41:45ZengDe GruyterJournal of Intelligent Systems2191-026X2024-04-0133112210.1515/jisys-2023-0179Detection of sickle cell disease using deep neural networks and explainable artificial intelligenceGoswami Neelankit Gautam0Goswami Anushree1Sampathila Niranjana2Bairy Muralidhar G.3Chadaga Krishnaraj4Belurkar Sushma5Department of Biomedical Engineering, Manipal Institute of Technology (MIT), Manipal Academy of Higher Education, Manipal, 576104, IndiaDepartment of Biomedical Engineering, Manipal Institute of Technology (MIT), Manipal Academy of Higher Education, Manipal, 576104, IndiaDepartment of Biomedical Engineering, Manipal Institute of Technology (MIT), Manipal Academy of Higher Education, Manipal, 576104, IndiaDepartment of Biomedical Engineering, Manipal Institute of Technology (MIT), Manipal Academy of Higher Education, Manipal, 576104, IndiaDepartment of Computer Science and Engineering, Manipal Institute of Technology (MIT), Manipal Academy of Higher Education, Manipal, 576104, IndiaDepartment of Pathology, Kasturba Medical College, Manipal Academy of Higer Education (MAHE), Manipal, 576104, IndiaSickle cell disease (SCD), a blood disorder that transforms the shape of red blood cells into a distinctive sickle form, is a major concern as it not only compromises the blood’s oxygen-carrying capacity but also poses significant health risks, ranging from weakness to paralysis and, in severe cases, even fatality. This condition not only underscores the pressing need for innovative solutions but also encapsulates the broader challenges faced by medical professionals, including delayed treatment, protracted processes, and the potential for subjective errors in diagnosis and classification. Consequently, the application of artificial intelligence (AI) in healthcare has emerged as a transformative force, inspiring multidisciplinary efforts to overcome the complexities associated with SCD and enhance diagnostic accuracy and treatment outcomes. The use of transfer learning helps to extract features from the input dataset and give an accurate prediction. We analyse and compare the performance parameters of three distinct models for this purpose: GoogLeNet, ResNet18, and ResNet50. The best results were shown by the ResNet50 model, with an accuracy of 94.90%. Explainable AI is the best approach for transparency and confirmation of the predictions made by the classifiers. This research utilizes Grad-CAM to interpret and make the models more reliable. Therefore, this specific approach benefits pathologists through its speed, precision, and accuracy of classification of sickle cells.https://doi.org/10.1515/jisys-2023-0179deep learningexplainable artificial intelligencegrad-camsickle cell disease |
| spellingShingle | Goswami Neelankit Gautam Goswami Anushree Sampathila Niranjana Bairy Muralidhar G. Chadaga Krishnaraj Belurkar Sushma Detection of sickle cell disease using deep neural networks and explainable artificial intelligence Journal of Intelligent Systems deep learning explainable artificial intelligence grad-cam sickle cell disease |
| title | Detection of sickle cell disease using deep neural networks and explainable artificial intelligence |
| title_full | Detection of sickle cell disease using deep neural networks and explainable artificial intelligence |
| title_fullStr | Detection of sickle cell disease using deep neural networks and explainable artificial intelligence |
| title_full_unstemmed | Detection of sickle cell disease using deep neural networks and explainable artificial intelligence |
| title_short | Detection of sickle cell disease using deep neural networks and explainable artificial intelligence |
| title_sort | detection of sickle cell disease using deep neural networks and explainable artificial intelligence |
| topic | deep learning explainable artificial intelligence grad-cam sickle cell disease |
| url | https://doi.org/10.1515/jisys-2023-0179 |
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