Timely Diagnosis of Acute Lymphoblastic Leukemia Using Artificial Intelligence-Oriented Deep Learning Methods

Background. Leukemia is fatal cancer in both children and adults and is divided into acute and chronic. Acute lymphoblastic leukemia (ALL) is a subtype of this cancer. Early diagnosis of this disease can have a significant impact on the treatment of this disease. Computational intelligence-oriented...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Sorayya Rezayi, Niloofar Mohammadzadeh, Hamid Bouraghi, Soheila Saeedi, Ali Mohammadpour
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/6a6b1f2205284167937b142f0f5b1b06
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:6a6b1f2205284167937b142f0f5b1b06
record_format	dspace
spelling	oai:doaj.org-article:6a6b1f2205284167937b142f0f5b1b062021-11-22T01:10:44ZTimely Diagnosis of Acute Lymphoblastic Leukemia Using Artificial Intelligence-Oriented Deep Learning Methods1687-527310.1155/2021/5478157https://doaj.org/article/6a6b1f2205284167937b142f0f5b1b062021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/5478157https://doaj.org/toc/1687-5273Background. Leukemia is fatal cancer in both children and adults and is divided into acute and chronic. Acute lymphoblastic leukemia (ALL) is a subtype of this cancer. Early diagnosis of this disease can have a significant impact on the treatment of this disease. Computational intelligence-oriented techniques can be used to help physicians identify and classify ALL rapidly. Materials and Method. In this study, the utilized dataset was collected from a CodaLab competition to classify leukemic cells from normal cells in microscopic images. Two famous deep learning networks, including residual neural network (ResNet-50) and VGG-16 were employed. These two networks are already trained by our assigned parameters, meaning we did not use the stored weights; we adjusted the weights and learning parameters too. Also, a convolutional network with ten convolutional layers and 2∗2 max-pooling layers—with strides 2—was proposed, and six common machine learning techniques were developed to classify acute lymphoblastic leukemia into two classes. Results. The validation accuracies (the mean accuracy of training and test networks for 100 training cycles) of the ResNet-50, VGG-16, and the proposed convolutional network were found to be 81.63%, 84.62%, and 82.10%, respectively. Among applied machine learning methods, the lowest obtained accuracy was related to multilayer perceptron (27.33%) and highest for random forest (81.72%). Conclusion. This study showed that the proposed convolutional neural network has optimal accuracy in the diagnosis of ALL. By comparing various convolutional neural networks and machine learning methods in diagnosing this disease, the convolutional neural network achieved good performance and optimal execution time without latency. This proposed network is less complex than the two pretrained networks and can be employed by pathologists and physicians in clinical systems for leukemia diagnosis.Sorayya RezayiNiloofar MohammadzadehHamid BouraghiSoheila SaeediAli MohammadpourHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENComputational Intelligence and Neuroscience, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Sorayya Rezayi Niloofar Mohammadzadeh Hamid Bouraghi Soheila Saeedi Ali Mohammadpour Timely Diagnosis of Acute Lymphoblastic Leukemia Using Artificial Intelligence-Oriented Deep Learning Methods
description	Background. Leukemia is fatal cancer in both children and adults and is divided into acute and chronic. Acute lymphoblastic leukemia (ALL) is a subtype of this cancer. Early diagnosis of this disease can have a significant impact on the treatment of this disease. Computational intelligence-oriented techniques can be used to help physicians identify and classify ALL rapidly. Materials and Method. In this study, the utilized dataset was collected from a CodaLab competition to classify leukemic cells from normal cells in microscopic images. Two famous deep learning networks, including residual neural network (ResNet-50) and VGG-16 were employed. These two networks are already trained by our assigned parameters, meaning we did not use the stored weights; we adjusted the weights and learning parameters too. Also, a convolutional network with ten convolutional layers and 2∗2 max-pooling layers—with strides 2—was proposed, and six common machine learning techniques were developed to classify acute lymphoblastic leukemia into two classes. Results. The validation accuracies (the mean accuracy of training and test networks for 100 training cycles) of the ResNet-50, VGG-16, and the proposed convolutional network were found to be 81.63%, 84.62%, and 82.10%, respectively. Among applied machine learning methods, the lowest obtained accuracy was related to multilayer perceptron (27.33%) and highest for random forest (81.72%). Conclusion. This study showed that the proposed convolutional neural network has optimal accuracy in the diagnosis of ALL. By comparing various convolutional neural networks and machine learning methods in diagnosing this disease, the convolutional neural network achieved good performance and optimal execution time without latency. This proposed network is less complex than the two pretrained networks and can be employed by pathologists and physicians in clinical systems for leukemia diagnosis.
format	article
author	Sorayya Rezayi Niloofar Mohammadzadeh Hamid Bouraghi Soheila Saeedi Ali Mohammadpour
author_facet	Sorayya Rezayi Niloofar Mohammadzadeh Hamid Bouraghi Soheila Saeedi Ali Mohammadpour
author_sort	Sorayya Rezayi
title	Timely Diagnosis of Acute Lymphoblastic Leukemia Using Artificial Intelligence-Oriented Deep Learning Methods
title_short	Timely Diagnosis of Acute Lymphoblastic Leukemia Using Artificial Intelligence-Oriented Deep Learning Methods
title_full	Timely Diagnosis of Acute Lymphoblastic Leukemia Using Artificial Intelligence-Oriented Deep Learning Methods
title_fullStr	Timely Diagnosis of Acute Lymphoblastic Leukemia Using Artificial Intelligence-Oriented Deep Learning Methods
title_full_unstemmed	Timely Diagnosis of Acute Lymphoblastic Leukemia Using Artificial Intelligence-Oriented Deep Learning Methods
title_sort	timely diagnosis of acute lymphoblastic leukemia using artificial intelligence-oriented deep learning methods
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/6a6b1f2205284167937b142f0f5b1b06
work_keys_str_mv	AT sorayyarezayi timelydiagnosisofacutelymphoblasticleukemiausingartificialintelligenceorienteddeeplearningmethods AT niloofarmohammadzadeh timelydiagnosisofacutelymphoblasticleukemiausingartificialintelligenceorienteddeeplearningmethods AT hamidbouraghi timelydiagnosisofacutelymphoblasticleukemiausingartificialintelligenceorienteddeeplearningmethods AT soheilasaeedi timelydiagnosisofacutelymphoblasticleukemiausingartificialintelligenceorienteddeeplearningmethods AT alimohammadpour timelydiagnosisofacutelymphoblasticleukemiausingartificialintelligenceorienteddeeplearningmethods
_version_	1718418342823329792

Timely Diagnosis of Acute Lymphoblastic Leukemia Using Artificial Intelligence-Oriented Deep Learning Methods

Ejemplares similares