Malaria Detection In Human Cells Using Deep Learning

الموضوعات

علوم تطبيقية وتكنولوجية

الملخص

This master’s report presents an automatic identification of Malaria infected cells in human blood samples using deep learning methods. We used a dataset of 27,558 images divided equally into infected and uninfected cells. Various machine learning and deep learning techniques used to solve this task. However, practical performance has not proven sufficient yet. In this respect, we built a system that can discriminate the infected cells from the uninfected ones. We proposed three different deep learning architectures, convolutional neural network (CNN), AutoEncoder and transfer learning using pretrained model (VGG16). The best result went to the CNN model which showed superiority over the AutoEncoder and transfer learning model (VGG16) in all performance indicators such as sensitivity (99.78%), specificity (99.95%) precision (99.87%) and F1 score (99,87%).

روابط وملفات

التعريف والنوع

رقم الوثيقة

074289e8-bd17-44c1-b18f-fabd2d042dc8

رقم العقد

0

نوع الوسائط

Crawler

نوع المحتوى

الرسائل العلمية

صيغة المصدر

ماستر (LMD)

نوع الملف

pdf text

أسماء الملفات

2263573_1.pdf

بيانات النشر

ألقاب المؤلفين

[{"name_ar":" Ahmed Cherif Mazari","title_ar":"اعداد","title_en":"Preparation"},{"name_ar":"Mourad Benaferi ","title_ar":"اشراف","title_en":"Supervision"},{"name_ar":"Mourad Benaferi Mourad ","title_ar":"اعداد","title_en":"Preparation"},{"name_ar":"Youcef Benabderrahmane ","title_ar":"اشراف","title_en":"Supervision"},{"name_ar":"Youcef Benabderrahmane ","title_ar":"اعداد","title_en":"Preparation"}]

اللغة

French

المصدر والدورية

اسم المصدر

Malaria Detection In Human Cells Using Deep Learning

المحتوى والصفحات

عدد الصفحات

0

كلمات الباحثين

Deep learning CNN AutoEncoder VGG16

إشراف وإعداد

الإشراف

Mourad Benaferi , Youcef Benabderrahmane

الإعداد

Ahmed Cherif Mazari, Mourad Benaferi Mourad , Youcef Benabderrahmane

الاقتباسات الببليوغرافية