Acute Myeloid Leukemia (AML) Detection Using AlexNet Model

Autor: Maneela Shaheen, Rafiullah Khan, R. R. Biswal, Mohib Ullah, Atif Khan, M. Irfan Uddin, Mahdi Zareei, Abdul Waheed
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Complexity, Vol 2021 (2021)
Druh dokumentu: article
ISSN: 1076-2787
1099-0526
DOI: 10.1155/2021/6658192
Popis: 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.
Databáze: Directory of Open Access Journals
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