MalariaMalaria is an epidemic disease which causes by parasites that are spread through bites from infected Anopheles mosquitoes. Through early diagnosisDiagnosis and timely treatment, malariaMalaria death rates can be decreased and prevented. But the manual examination of blood smears by laboratory technicians is time-consuming and probable chances of human errors. Considering this need, in this paper, we are demonstrating a proposed system deployed over a WebApp could help laboratories to perform fast and accurate tests. For the malariaMalaria parasite detection in the blood cells, we proposed our custom CNN modelCustom CNN model and even implemented the dataset on the Transfer Learning model (VGG19) to compare the results. We got the highest accuracy of 97.74% for our CNN model which is the simplest of the current models as it prioritizes only important parameters, provides great computational efficiency, and is realistic for implementation. Additionally, a malariaMalaria outbreak warning system is proposed to warn the people in localities when there is availing risk based on climatic factors. A logistic regressionLogistic Regression model implemented by Gradient Descent Optimizer is deployed which predicts the malariaMalaria outbreak from the live weather forecastForecast inputs from the OpenWeather API and displays the probability of the outbreak to the users with one click.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Malaria Parasite Detection and Outbreak Warning System Using Deep Learning


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Sharma, Sanjay (Herausgeber:in) / Subudhi, Bidyadhar (Herausgeber:in) / Sahu, Umesh Kumar (Herausgeber:in) / Areefa (Autor:in) / Koneru, Sivarama Krishna (Autor:in) / Pragathi, Kota (Autor:in) / Rishitha, Koyyada (Autor:in)

    Kongress:

    International Conference on Robotics, Control, Automation and Artificial Intelligence ; 2022 November 24, 2022 - November 26, 2022



    Erscheinungsdatum :

    2023-11-18


    Format / Umfang :

    16 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Automated Malaria Parasite Detection

    Rowa, Per / Antonsson, Dan | BASE | 1977

    Freier Zugriff

    Parasite detection and identification for automated thin blood film malaria diagnosis

    Tek, F. B. / Dempster, A. G. / Kale, I. | British Library Online Contents | 2010


    A Forward Collision Warning System Using Deep Reinforcement Learning

    Dargahi, Javad / Zadeh, Mehrdad / Fekri, Pedram et al. | SAE Technical Papers | 2020



    Early warning braking method based on deep learning

    LI HANG / WANG BIN / LIU SHUAI et al. | Europäisches Patentamt | 2023

    Freier Zugriff