Acute Myeloid Leukemia (AML) Detection Using AlexNet Model
Acute Myeloid Leukemia (AML) is a kind of fatal blood cancer with a high death rate caused by abnormal cells’ rapid growth in the human body. The usual method to detect AML is the manual microscopic examination of the blood sample, which is tedious and time-consuming and requires a skilled medical o...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6658192 |
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| author | Maneela Shaheen Rafiullah Khan R. R. Biswal Mohib Ullah Atif Khan M. Irfan Uddin Mahdi Zareei Abdul Waheed |
| author_facet | Maneela Shaheen Rafiullah Khan R. R. Biswal Mohib Ullah Atif Khan M. Irfan Uddin Mahdi Zareei Abdul Waheed |
| author_sort | Maneela Shaheen |
| collection | DOAJ |
| description | Acute Myeloid Leukemia (AML) is a kind of fatal blood cancer with a high death rate caused by abnormal cells’ rapid growth in the human body. The usual method to detect AML is the manual microscopic examination of the blood sample, which is tedious and time-consuming and requires a skilled medical operator for accurate detection. In this work, we proposed an AlexNet-based classification model to detect Acute Myeloid Leukemia (AML) in microscopic blood images and compared its performance with LeNet-5-based model in Precision, Recall, Accuracy, and Quadratic Loss. The experiments are conducted on a dataset of four thousand blood smear samples. The results show that AlexNet was able to identify 88.9% of images correctly with 87.4% precision and 98.58% accuracy, whereas LeNet-5 correctly identified 85.3% of images with 83.6% precision and 96.25% accuracy. |
| format | Article |
| id | doaj-art-9bfe194acca84492b75d016da689c020 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-9bfe194acca84492b75d016da689c0202025-02-03T01:00:17ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66581926658192Acute Myeloid Leukemia (AML) Detection Using AlexNet ModelManeela Shaheen0Rafiullah Khan1R. R. Biswal2Mohib Ullah3Atif Khan4M. Irfan Uddin5Mahdi Zareei6Abdul Waheed7Institute of Computer Science and Information Technology, The University of Agriculture, Peshawar, PakistanInstitute of Computer Science and Information Technology, The University of Agriculture, Peshawar, PakistanTecnologico de Monterrey, School of Engineering and Sciences, Zapopan, MexicoInstitute of Computer Science and Information Technology, The University of Agriculture, Peshawar, PakistanDepartment of Computer Science, Islamia College Peshawar, Peshawar, KP, PakistanInstitute of Computing, Kohat University of Science and Technology, Kohat, PakistanTecnologico de Monterrey, School of Engineering and Sciences, Zapopan, MexicoDepartment of Information Technology, Hazara University Mansehra, Mansehra 21120, PakistanAcute Myeloid Leukemia (AML) is a kind of fatal blood cancer with a high death rate caused by abnormal cells’ rapid growth in the human body. The usual method to detect AML is the manual microscopic examination of the blood sample, which is tedious and time-consuming and requires a skilled medical operator for accurate detection. In this work, we proposed an AlexNet-based classification model to detect Acute Myeloid Leukemia (AML) in microscopic blood images and compared its performance with LeNet-5-based model in Precision, Recall, Accuracy, and Quadratic Loss. The experiments are conducted on a dataset of four thousand blood smear samples. The results show that AlexNet was able to identify 88.9% of images correctly with 87.4% precision and 98.58% accuracy, whereas LeNet-5 correctly identified 85.3% of images with 83.6% precision and 96.25% accuracy.http://dx.doi.org/10.1155/2021/6658192 |
| spellingShingle | Maneela Shaheen Rafiullah Khan R. R. Biswal Mohib Ullah Atif Khan M. Irfan Uddin Mahdi Zareei Abdul Waheed Acute Myeloid Leukemia (AML) Detection Using AlexNet Model Complexity |
| title | Acute Myeloid Leukemia (AML) Detection Using AlexNet Model |
| title_full | Acute Myeloid Leukemia (AML) Detection Using AlexNet Model |
| title_fullStr | Acute Myeloid Leukemia (AML) Detection Using AlexNet Model |
| title_full_unstemmed | Acute Myeloid Leukemia (AML) Detection Using AlexNet Model |
| title_short | Acute Myeloid Leukemia (AML) Detection Using AlexNet Model |
| title_sort | acute myeloid leukemia aml detection using alexnet model |
| url | http://dx.doi.org/10.1155/2021/6658192 |
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