Malaria Disease Recognition through Adaptive Deep Learning Models of Convolutional Neural Network

Militante, Sammy V.

doi:10.1109/icetas48360.2019.9117446

Cited by 21 publications

(5 citation statements)

References 23 publications

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“…Convolutional Neural Network (CNN) is a structured deep learning process that plays a groundbreaking push for a variety of applications focusing on computer vision and image-based applications [29]. The fields in which CNN is prevalently used are facial recognition, object recognition, classification of images [30], [31], etc. The components of the CNN model are shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Real-Time Facemask Recognition with Alarm System using Deep Learning

Militante

Dionisio

2020

2020 11th IEEE Control and System Graduate Research Colloquium (ICSGRC)

Self Cite

115

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In the background of the COVID-19 pandemic, institutions such as the academy suffer a great deal from practically closed globally if the current situation is not going to change. COVID-19 also known as Serious Acute Respiratory Syndrome Corona virus-2 is an infectious disease that is released from an infected sick person who speaks, sneezes, or coughs by respiratory droplets. This spreads quickly through close contact with anyone infected, or by touching objects or surfaces affected with a virus. There's still currently no vaccine available to protect against COVID-19 and preventing exposure to the virus seems to be the only method to safeguard ourselves. Wearing a facemask that covers the nose and mouth in a public setting and often washing hands or the use of at least 70% alcohol-based sanitizers is one way to avoid being exposed to the virus. Amid the advancement of technology, Deep Learning has proven its effectiveness in recognition and classification through image processing. The research study uses deep learning techniques in distinguishing facial recognition and recognize if the person is wearing a facemask or not. The dataset collected contains 25,000 images using 224x224 pixel resolution and achieved an accuracy rate of 96% as to the performance of the trained model. The system develops a Raspberry Pi-based real-time facemask recognition that alarms and captures the facial image if the person detected is not wearing a facemask. This study is beneficial in combating the spread of the virus and avoiding contact with the virus.

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Section: Methodsmentioning

confidence: 99%

Real-Time Facemask Recognition with Alarm System using Deep Learning

Militante

Dionisio

2020

2020 11th IEEE Control and System Graduate Research Colloquium (ICSGRC)

Self Cite

115

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“…After training on 30 epochs the accuracy achieved at 93%. -Sammy V. and et al 2019 [10]. The study aimed to evaluate performance of CNN in several architecture using transfer leaning (TL) to detecting Malaria.…”

Section: Related Workmentioning

confidence: 99%

Diagnosis of Malaria Infected Blood Cell Digital Images using Deep Convolutional Neural Networks

Jabbar

Radhi

2022

eijs

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Automated medical diagnosis is an important topic, especially in detection and classification of diseases. Malaria is one of the most widespread diseases, with more than 200 million cases, according to the 2016 WHO report. Malaria is usually diagnosed using thin and thick blood smears under a microscope. However, proper diagnosis is difficult, especially in poor countries where the disease is most widespread. Therefore, automatic diagnostics helps in identifying the disease through images of red blood cells, with the use of machine learning techniques and digital image processing. This paper presents an accurate model using a Deep Convolutional Neural Network build from scratch. The paper also proposed three CNN models each one trained on the Malaria RBC dataset with different architectures for handling the classification tasks. Furthermore, disadvantage of the traditional method of using transfer learning, and how to control model complexity to achieve better performance was discussed. The dropout regularization technique was used to avoid overfitting problems and minimize validation loss. Applying Data Augmentation technique to avoid the problem of small data in training of proposed models, which is a very common problem in medical dataset. Finally, removing noise in Malaria images using a Median blur filter, and studying how effects of that on training CNN models. According to the classification results, the proposed model achieved better classification results at accuracy 99.22 on the original Malaria RBCs dataset, and it has the best performance comparing with related work.

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“…Plasmodic parasites that are the estimate unit communicates by chomps of the tangled female class mosquitos can also cause contamination to be a dangerous irresistible mosquito-borne disease. There are 5 parasites, but two sorts -P. Falciparum and P. Vivaxwhich cause most of the illnesses [2] in a unit of estimation. In the absence of a dipterone tear in the partner, a dipterone transported parasite enters your blood and begins to obliterate red oxygen platelets (RBC).…”

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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Malaria Disease Recognition through Adaptive Deep Learning Models of Convolutional Neural Network

Cited by 21 publications

References 23 publications

Real-Time Facemask Recognition with Alarm System using Deep Learning

Real-Time Facemask Recognition with Alarm System using Deep Learning

Diagnosis of Malaria Infected Blood Cell Digital Images using Deep Convolutional Neural Networks

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

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