Intelligent Medical IoT-Enabled Automated Microscopic Image Diagnosis of Acute Blood Cancers
Blood cancer, or leukemia, has a negative impact on the blood and/or bone marrow of children and adults. Acute lymphocytic leukemia (ALL) and acute myeloid leukemia (AML) are two sub-types of acute leukemia. The Internet of Medical Things (IoMT) and artificial intelligence have allowed for the devel...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/6/2348 |
| _version_ | 1797442301072506880 |
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| author | Mohamed Esmail Karar Bandar Alotaibi Munif Alotaibi |
| author_facet | Mohamed Esmail Karar Bandar Alotaibi Munif Alotaibi |
| author_sort | Mohamed Esmail Karar |
| collection | DOAJ |
| description | Blood cancer, or leukemia, has a negative impact on the blood and/or bone marrow of children and adults. Acute lymphocytic leukemia (ALL) and acute myeloid leukemia (AML) are two sub-types of acute leukemia. The Internet of Medical Things (IoMT) and artificial intelligence have allowed for the development of advanced technologies to assist in recently introduced medical procedures. Hence, in this paper, we propose a new intelligent IoMT framework for the automated classification of acute leukemias using microscopic blood images. The workflow of our proposed framework includes three main stages, as follows. First, blood samples are collected by wireless digital microscopy and sent to a cloud server. Second, the cloud server carries out automatic identification of the blood conditions—either leukemias or healthy—utilizing our developed generative adversarial network (GAN) classifier. Finally, the classification results are sent to a hematologist for medical approval. The developed GAN classifier was successfully evaluated on two public data sets: ALL-IDB and ASH image bank. It achieved the best accuracy scores of 98.67% for binary classification (ALL or healthy) and 95.5% for multi-class classification (ALL, AML, and normal blood cells), when compared with existing state-of-the-art methods. The results of this study demonstrate the feasibility of our proposed IoMT framework for automated diagnosis of acute leukemia tests. Clinical realization of this blood diagnosis system is our future work. |
| first_indexed | 2024-03-09T12:39:51Z |
| format | Article |
| id | doaj.art-680c9f9a6fff4b389b98bdf10977d11e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T12:39:51Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-680c9f9a6fff4b389b98bdf10977d11e2023-11-30T22:19:49ZengMDPI AGSensors1424-82202022-03-01226234810.3390/s22062348Intelligent Medical IoT-Enabled Automated Microscopic Image Diagnosis of Acute Blood CancersMohamed Esmail Karar0Bandar Alotaibi1Munif Alotaibi2College of Computing and Information Technology, Shaqra University, P.O. Box 33, Shaqra 11961, Saudi ArabiaDepartment of Information Technology, University of Tabuk, Tabuk 47731, Saudi ArabiaCollege of Computing and Information Technology, Shaqra University, P.O. Box 33, Shaqra 11961, Saudi ArabiaBlood cancer, or leukemia, has a negative impact on the blood and/or bone marrow of children and adults. Acute lymphocytic leukemia (ALL) and acute myeloid leukemia (AML) are two sub-types of acute leukemia. The Internet of Medical Things (IoMT) and artificial intelligence have allowed for the development of advanced technologies to assist in recently introduced medical procedures. Hence, in this paper, we propose a new intelligent IoMT framework for the automated classification of acute leukemias using microscopic blood images. The workflow of our proposed framework includes three main stages, as follows. First, blood samples are collected by wireless digital microscopy and sent to a cloud server. Second, the cloud server carries out automatic identification of the blood conditions—either leukemias or healthy—utilizing our developed generative adversarial network (GAN) classifier. Finally, the classification results are sent to a hematologist for medical approval. The developed GAN classifier was successfully evaluated on two public data sets: ALL-IDB and ASH image bank. It achieved the best accuracy scores of 98.67% for binary classification (ALL or healthy) and 95.5% for multi-class classification (ALL, AML, and normal blood cells), when compared with existing state-of-the-art methods. The results of this study demonstrate the feasibility of our proposed IoMT framework for automated diagnosis of acute leukemia tests. Clinical realization of this blood diagnosis system is our future work.https://www.mdpi.com/1424-8220/22/6/2348acute leukemiagenerative adversarial networkscomputer-aided diagnosisinternet of medical thingswireless microscopic imaging |
| spellingShingle | Mohamed Esmail Karar Bandar Alotaibi Munif Alotaibi Intelligent Medical IoT-Enabled Automated Microscopic Image Diagnosis of Acute Blood Cancers Sensors acute leukemia generative adversarial networks computer-aided diagnosis internet of medical things wireless microscopic imaging |
| title | Intelligent Medical IoT-Enabled Automated Microscopic Image Diagnosis of Acute Blood Cancers |
| title_full | Intelligent Medical IoT-Enabled Automated Microscopic Image Diagnosis of Acute Blood Cancers |
| title_fullStr | Intelligent Medical IoT-Enabled Automated Microscopic Image Diagnosis of Acute Blood Cancers |
| title_full_unstemmed | Intelligent Medical IoT-Enabled Automated Microscopic Image Diagnosis of Acute Blood Cancers |
| title_short | Intelligent Medical IoT-Enabled Automated Microscopic Image Diagnosis of Acute Blood Cancers |
| title_sort | intelligent medical iot enabled automated microscopic image diagnosis of acute blood cancers |
| topic | acute leukemia generative adversarial networks computer-aided diagnosis internet of medical things wireless microscopic imaging |
| url | https://www.mdpi.com/1424-8220/22/6/2348 |
| work_keys_str_mv | AT mohamedesmailkarar intelligentmedicaliotenabledautomatedmicroscopicimagediagnosisofacutebloodcancers AT bandaralotaibi intelligentmedicaliotenabledautomatedmicroscopicimagediagnosisofacutebloodcancers AT munifalotaibi intelligentmedicaliotenabledautomatedmicroscopicimagediagnosisofacutebloodcancers |