Acute Myeloid Leukemia (AML) detection using AlexNet model

Maneela Shaheen; Rafiullah Khan; R. R. Biswal; Mohib Ullah; Atif Khan; M. Irfan Uddin; Mahdi Zareei; Abdul Waheed

doi:10.1155/2021/6658192

Acute Myeloid Leukemia (AML) detection using AlexNet model

Maneela Shaheen, Rafiullah Khan^*, R. R. Biswal, Mohib Ullah, Atif Khan, M. Irfan Uddin, Mahdi Zareei^*, Abdul Waheed

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

34 Citations (Scopus)

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Abstract

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.

Original language	English
Article number	6658192
Pages (from-to)	1-8
Number of pages	8
Journal	Complexity
Volume	2021
DOIs	https://doi.org/10.1155/2021/6658192
Publication status	Published - 2021
Externally published	Yes

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Copyright the Author(s) 2021. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

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Publisher version (open access)Final published version, 1.59 MBLicence: CC BY

Cite this

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title = "Acute Myeloid Leukemia (AML) detection using AlexNet model",

abstract = "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.",

author = "Maneela Shaheen and Rafiullah Khan and Biswal, {R. R.} and Mohib Ullah and Atif Khan and Uddin, {M. Irfan} and Mahdi Zareei and Abdul Waheed",

note = "Copyright the Author(s) 2021. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.",

year = "2021",

doi = "10.1155/2021/6658192",

language = "English",

volume = "2021",

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AU - Shaheen, Maneela

AU - Khan, Rafiullah

AU - Biswal, R. R.

AU - Ullah, Mohib

AU - Khan, Atif

AU - Uddin, M. Irfan

AU - Zareei, Mahdi

AU - Waheed, Abdul

N1 - Copyright the Author(s) 2021. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

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JF - Complexity

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Acute Myeloid Leukemia (AML) detection using AlexNet model

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