Acute Myeloid Leukemia (AML) Detection Using AlexNet Model

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...

Full description

Saved in:
Bibliographic Details
Published in:Complexity (New York, N.Y.) Vol. 2021; no. 1
Main Authors: Shaheen, Maneela, Khan, Rafiullah, Biswal, R. R., Ullah, Mohib, Khan, Atif, Uddin, M. Irfan, Zareei, Mahdi, Waheed, Abdul
Format: Journal Article
Language:English
Published: Hoboken Hindawi 2021
Hindawi Limited
Hindawi-Wiley
Subjects:
Accuracy
Algorithms
Bacteria
Blood
Cancer
Classification
Datasets
Deep learning
Disease
Hematology
Human body
Image classification
Immune system
Infections
Leukemia
Lymphocytes
Medical imaging
Model accuracy
Neural networks
Neutrophils
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1076-2787
1099-0526
DOI:10.1155/2021/6658192