International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 04 | April 2023
p-ISSN: 2395-0072
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BLOOD TISSUE IMAGE TO IDENTIFY MALARIA DISEASE CLASSIFICATION T.Keerthika1, B.Akalya2, G.Kirithika3, S.Sineka4 1-4 Student of Department of Information Technology, Meenakshi College of Engineering, Chennai, Tamil Nadu,
India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Plasmodium falciparum malaria epidemics
We will then develop and train deep learning models, such as convolutional neural networks and deep belief networks, on this dataset to predict the presence of malaria. Finally, we will evaluate the performance of our models using a variety of metrics, such as accuracy, precision, and recall, and compare them to traditional approaches for malaria diagnosis.
are common and often lethal, according to reports. Through the use of meteorological characteristics that are determinants of transmission potential, epidemics have been formally attempted to be predicted. Regarding the relative weight and predictive power of these criteria, however, there is little agreement. To identify precise and significant indicators for epidemic prediction we are using ASKalgorithm, it is essential to comprehend the causes of variance. In this study, we extracted several blood cell properties and used convolutional neural network-based models to identify malaria in blood tissue images using structured analysis. Convolutional neural networks (CNNs) were used in deep learning to successfully classify malaria blood tissues. It was described as a new technique that offers effective categorization detection.
The ultimate objective of this research is to create a reliable and effective automated method for diagnosing malaria that may be employed in environments with limited resources. We can enhance malaria early diagnosis and treatment, lowering the total health burden of the illness, by utilizing deep learning to analyze blood tissue pictures.
2. PROPOSED SYSTEM 2.1 ARCHITECTURE DIAGRAM
Key Words: Malaria detection, Plasmodium parasite, Transfer learning, Convolutional neural networks, Computer aided design (CAD), Alex net, Lenet
1. INTRODUCTION Millions of people suffer from malaria, a parasite illness that is most prevalent in underdeveloped nations. Effective malaria treatment and disease management depend on an early and precise diagnosis. Currently, the most used approach for diagnosing malaria is microscopic analysis of blood smears. This strategy of diagnosing malaria is labor and time-intensive, hence automated and effective methods are required. A branch of artificial intelligence called deep learning has demonstrated promise in a number of picture categorization tasks. Deep learning for malaria prediction using blood tissue pictures has attracted more attention in recent years. Researchers have created machine learning models that can accurately predict the presence of malaria using vast datasets of blood tissue pictures that are both malariapositive and malaria-negative.
EXPLANATION Upload the blood tissue images as dataset. The datasets is preprocessed such as image reshaping, resizing and conversion to the array form. The train dataset is used to train the CNN model.
In this project, we will explore the use of deep learning for malaria prediction using blood tissue images. We will begin by acquiring a dataset of blood tissue images from malariapositive and malaria-negative patients, and we will preprocess the data to ensure quality and standardization.
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After the model is trained, the blood tissue image dataset undergo the testing model. The model is deployed using Django framework. Atlast the malaria is predicted.
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