An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification

Leukemia is a kind of blood cancer that influences people of all ages and is one of the leading causes of death worldwide. Acute lymphoblastic leukemia (ALL) is the most widely recognized type of leukemia found in the bone marrow of the human body. Traditional disease diagnostic techniques like bloo...

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Autores principales:	Muhammad Zakir Ullah, Yuanjie Zheng, Jingqi Song, Sehrish Aslam, Chenxi Xu, Gogo Dauda Kiazolu, Liping Wang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	acute lymphoblastic leukemia medical image classification convolutional neural networks efficient channel attention Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/9a05545bd79d4853b6cca2a180d0d0a7
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spelling	oai:doaj.org-article:9a05545bd79d4853b6cca2a180d0d0a72021-11-25T16:34:44ZAn Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification10.3390/app1122106622076-3417https://doaj.org/article/9a05545bd79d4853b6cca2a180d0d0a72021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10662https://doaj.org/toc/2076-3417Leukemia is a kind of blood cancer that influences people of all ages and is one of the leading causes of death worldwide. Acute lymphoblastic leukemia (ALL) is the most widely recognized type of leukemia found in the bone marrow of the human body. Traditional disease diagnostic techniques like blood and bone marrow examinations are slow and painful, resulting in the demand for non-invasive and fast methods. This work presents a non-invasive, convolutional neural network (CNN) based approach that utilizes medical images to perform the diagnosis task. The proposed solution consisting of a CNN-based model uses an attention module called Efficient Channel Attention (ECA) with the visual geometry group from oxford (VGG16) to extract better quality deep features from the image dataset, leading to better feature representation and better classification results. The proposed method shows that the ECA module helps to overcome morphological similarities between ALL cancer and healthy cell images. Various augmentation techniques are also employed to increase the quality and quantity of training data. We used the classification of normal vs. malignant cells (C-NMC) dataset and divided it into seven folds based on subject-level variability, which is usually ignored in previous methods. Experimental results show that our proposed CNN model can successfully extract deep features and achieved an accuracy of 91.1%. The obtained findings show that the proposed method may be utilized to diagnose ALL and would help pathologists.Muhammad Zakir UllahYuanjie ZhengJingqi SongSehrish AslamChenxi XuGogo Dauda KiazoluLiping WangMDPI AGarticleacute lymphoblastic leukemiamedical image classificationconvolutional neural networksefficient channel attentionTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10662, p 10662 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	acute lymphoblastic leukemia medical image classification convolutional neural networks efficient channel attention Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	acute lymphoblastic leukemia medical image classification convolutional neural networks efficient channel attention Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Muhammad Zakir Ullah Yuanjie Zheng Jingqi Song Sehrish Aslam Chenxi Xu Gogo Dauda Kiazolu Liping Wang An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification
description	Leukemia is a kind of blood cancer that influences people of all ages and is one of the leading causes of death worldwide. Acute lymphoblastic leukemia (ALL) is the most widely recognized type of leukemia found in the bone marrow of the human body. Traditional disease diagnostic techniques like blood and bone marrow examinations are slow and painful, resulting in the demand for non-invasive and fast methods. This work presents a non-invasive, convolutional neural network (CNN) based approach that utilizes medical images to perform the diagnosis task. The proposed solution consisting of a CNN-based model uses an attention module called Efficient Channel Attention (ECA) with the visual geometry group from oxford (VGG16) to extract better quality deep features from the image dataset, leading to better feature representation and better classification results. The proposed method shows that the ECA module helps to overcome morphological similarities between ALL cancer and healthy cell images. Various augmentation techniques are also employed to increase the quality and quantity of training data. We used the classification of normal vs. malignant cells (C-NMC) dataset and divided it into seven folds based on subject-level variability, which is usually ignored in previous methods. Experimental results show that our proposed CNN model can successfully extract deep features and achieved an accuracy of 91.1%. The obtained findings show that the proposed method may be utilized to diagnose ALL and would help pathologists.
format	article
author	Muhammad Zakir Ullah Yuanjie Zheng Jingqi Song Sehrish Aslam Chenxi Xu Gogo Dauda Kiazolu Liping Wang
author_facet	Muhammad Zakir Ullah Yuanjie Zheng Jingqi Song Sehrish Aslam Chenxi Xu Gogo Dauda Kiazolu Liping Wang
author_sort	Muhammad Zakir Ullah
title	An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification
title_short	An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification
title_full	An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification
title_fullStr	An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification
title_full_unstemmed	An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification
title_sort	attention-based convolutional neural network for acute lymphoblastic leukemia classification
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/9a05545bd79d4853b6cca2a180d0d0a7
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An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification

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