Leveraging Deep Learning Techniques for Malaria Parasite Detection Using Mobile Application
المؤلفون المشاركون
Cheikhrouhou, Omar
Masud, Mehedi
Hossain, M. Shamim
Alhumyani, Hesham
Alshamrani, Sultan S.
Ibrahim, Saleh
Muhammad, Ghulam
Shorfuzzaman, Mohammad
المصدر
Wireless Communications and Mobile Computing
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-15، 15ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-07-08
دولة النشر
مصر
عدد الصفحات
15
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
Malaria is a contagious disease that affects millions of lives every year.
Traditional diagnosis of malaria in laboratory requires an experienced person and careful inspection to discriminate healthy and infected red blood cells (RBCs).
It is also very time-consuming and may produce inaccurate reports due to human errors.
Cognitive computing and deep learning algorithms simulate human intelligence to make better human decisions in applications like sentiment analysis, speech recognition, face detection, disease detection, and prediction.
Due to the advancement of cognitive computing and machine learning techniques, they are now widely used to detect and predict early disease symptoms in healthcare field.
With the early prediction results, healthcare professionals can provide better decisions for patient diagnosis and treatment.
Machine learning algorithms also aid the humans to process huge and complex medical datasets and then analyze them into clinical insights.
This paper looks for leveraging deep learning algorithms for detecting a deadly disease, malaria, for mobile healthcare solution of patients building an effective mobile system.
The objective of this paper is to show how deep learning architecture such as convolutional neural network (CNN) which can be useful in real-time malaria detection effectively and accurately from input images and to reduce manual labor with a mobile application.
To this end, we evaluate the performance of a custom CNN model using a cyclical stochastic gradient descent (SGD) optimizer with an automatic learning rate finder and obtain an accuracy of 97.30% in classifying healthy and infected cell images with a high degree of precision and sensitivity.
This outcome of the paper will facilitate microscopy diagnosis of malaria to a mobile application so that reliability of the treatment and lack of medical expertise can be solved.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Masud, Mehedi& Alhumyani, Hesham& Alshamrani, Sultan S.& Cheikhrouhou, Omar& Ibrahim, Saleh& Muhammad, Ghulam…[et al.]. 2020. Leveraging Deep Learning Techniques for Malaria Parasite Detection Using Mobile Application. Wireless Communications and Mobile Computing،Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1214929
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Masud, Mehedi…[et al.]. Leveraging Deep Learning Techniques for Malaria Parasite Detection Using Mobile Application. Wireless Communications and Mobile Computing No. 2020 (2020), pp.1-15.
https://search.emarefa.net/detail/BIM-1214929
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Masud, Mehedi& Alhumyani, Hesham& Alshamrani, Sultan S.& Cheikhrouhou, Omar& Ibrahim, Saleh& Muhammad, Ghulam…[et al.]. Leveraging Deep Learning Techniques for Malaria Parasite Detection Using Mobile Application. Wireless Communications and Mobile Computing. 2020. Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1214929
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1214929
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر