An Effective Convolutional Neural Network for Classifying Red Blood Cells in Malaria Diseases

Quan, Quan; Wang, Jianxin; Liu, Liangliang

doi:10.1007/s12539-020-00367-7

Cited by 36 publications

(13 citation statements)

References 51 publications

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“…The focus on these techniques is to help health professionals. The authors propose various architectures such as ResNet, DenseNet, DPN92 or ADCN [ 30 ] for automated medical diagnosis of malaria. We compare our model results with other similar studies that can be found in the literature.…”

Section: Discussionmentioning

confidence: 99%

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An ensemble-based approach for automated medical diagnosis of malaria using EfficientNet

Marques

Ferreras

Díez

2022

Multimed Tools Appl

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Each year, more than 400,000 people die of malaria. Malaria is a mosquito-borne transmissible infection that affects humans and other animals. According to World Health Organization (WHO), 1.5 billion malaria cases and 7.6 million related deaths have been prevented from 2000 to 2019. Malaria is a disease that can be treated if early detected. We propose a support decision system for detecting malaria from microscopic peripheral blood cells images through convolutional neural networks (CNN). The proposed model is based on EfficientNetB0 architecture. The results are validated with 10-fold stratified cross-validation. This paper presents the classification findings using images from malaria patients and normal patients. The proposed approach is compared and outperforms the related work. Furthermore, the proposed ensemble method shows a recall value of 98.82%, a precision value of 97.74%, an F1-score of 98.28% and a ROC value of 99.76%. This work suggests that EfficientNet is a reliable architecture for automatic medical diagnostics of malaria. Supplementary Information The online version contains supplementary material available at 10.1007/s11042-022-12624-6.

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Section: Discussionmentioning

confidence: 99%

“…Quan et al [ 30 ] worked on a novel and lightweight model based on CNNs, combining concepts from dense and residual networks and employing attention mechanisms. The proposed method was entitled Attentive Dense Circular Net (ACDN).…”

Section: Related Workmentioning

confidence: 99%

An ensemble-based approach for automated medical diagnosis of malaria using EfficientNet

Marques

Ferreras

Díez

2022

Multimed Tools Appl

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“…Several studies have been proposed for malaria blood smear detection, segmentation, and classification [16,17]. Quan et al [18] utilized 27,558 malaria blood smear images from the National Institute of Health (NIH) and expanded wielding rotation, zooming, and flipping. An attention dense circular net (ADCN) was proposed for infected red blood cell classification.…”

Section: Figure 1: Cycle Of Microbe To Mosquito Transmissionmentioning

confidence: 99%

Malaria Blood Smear Classification Using Deep Learning and Best Features Selection

Imran¹,

Khan²,

Sharif³

et al. 2022

Computers, Materials &Amp; Continua

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Malaria is a critical health condition that affects both sultry and frigid region worldwide, giving rise to millions of cases of disease and thousands of deaths over the years. Malaria is caused by parasites that enter the human red blood cells, grow there, and damage them over time. Therefore, it is diagnosed by a detailed examination of blood cells under the microscope. This is the most extensively used malaria diagnosis technique, but it yields limited and unreliable results due to the manual human involvement. In this work, an automated malaria blood smear classification model is proposed, which takes images of both infected and healthy cells and preprocesses them in the L*a*b* color space by employing several contrast enhancement methods. Feature extraction is performed using two pretrained deep convolutional neural networks, DarkNet-53 and DenseNet-201. The features are subsequently agglutinated to be optimized through a nature-based feature reduction method called the whale optimization algorithm. Several classifiers are effectuated on the reduced features, and the achieved results excel in both accuracy and time compared to previously proposed methods.

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“…AI is one of the areas in the common world of registration [3]. The devices were made rational by a marvellous research arrangement [4]. Learning may also be a normal human behaviour, and is now a main machine.…”

Section: Literature Surveymentioning

confidence: 99%

An Efficient Image Classification of Malaria Parasite Using Convolutional Neural Network and ADAM Optimizer

Kumar¹

2021

TURCOMAT

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Machine learning can be a technique of nursing lysis that automatically develops an analytical model. It is a branch of synthetic intelligence that believes that systems are going to learn information, determine patterns of information and decide with degraded human intervention. Machine learning addresses the question of how computers can be constructed that improve mechanically through knowledge. It lies at the intersection of technology and statistics and at the center of artificial data and information science, one in all the quickest increasing technical fields of nowadays. Recent advances in machine learning were driven by the event of latest learning and theories also as by the constant explosion. The event of latest learning algorithms and also theory and the in-progress growth within the accessibility of on-line information also as low-priced computation crystal rectifier to recent progress within the field of machine learning. Additional evidence-based decision-making could be carried out in science, technology and trade, including healthcare, production, education and monetary modelling, enforcement and promotion, with adoption of mechanical learning techniques based on data-intensive methods. The results are also available. The infection can be a life-threatening disease. The bite of a nursing partner is often transmitted in dipterous Anopheles. In infected mosquitoes, plasmodium parasite is a gift. The parasite is discharged into your blood after you bite this dipterous insect once it bites you. Once your body is composed of the parasites, they mature into the liver. The mature parasites enter the blood for several days when red blood cells start to infect. In red blood cells, parasites increase over 48-72 hours, causing infected cells to divide. The parasites still infect red blood cells, which last 2 to 3 days in cycles. This paper is used for observation of protozoan infection with a deep learning idea.

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An Effective Convolutional Neural Network for Classifying Red Blood Cells in Malaria Diseases

Cited by 36 publications

References 51 publications

An ensemble-based approach for automated medical diagnosis of malaria using EfficientNet

An ensemble-based approach for automated medical diagnosis of malaria using EfficientNet

Malaria Blood Smear Classification Using Deep Learning and Best Features Selection

An Efficient Image Classification of Malaria Parasite Using Convolutional Neural Network and ADAM Optimizer

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