Detection of Malaria on an Erythrocyte Using Deep Residual Network

Moktar, Samir Mouhamad

dc.contributor.author	Moktar, Samir Mouhamad
dc.date.accessioned	2021-08-03T04:44:23Z
dc.date.available	2021-08-03T04:44:23Z
dc.date.issued	2019-11-20
dc.identifier.citation	[1] Artificial Neural Networks for Detection of Malaria in RBCs [2016] [2]CNN-Based Image Analysis for Malaria Diagnosis [2016] [3] Automated detection of malaria parasites on thick blood smears via mobile devices [2016] [4] Automatic Detection of Malaria Infected RBCs from a Focus Stack of Bright Field Microscope Slide Images [2016] [5] Deep Convolutional Neural Networks for Microscopy-Based Point of Care Diagnostics [2016] [6]Image Classification of Unlabeled Malaria Parasites in Red Blood Cells [2016] [7] Malaria Infected Erythrocyte Classification Based on the Histogram Features using Microscopic Images of Thin Blood Smear. [2016] [8] Applying Faster R-CNN for Object Detection on Malaria Images [2017] [9] Automated plasmodia recognition in microscopic images for diagnosis of malaria using CNN [2017] [10] Detection and Classification of Malaria in Thin Blood Slide Images [2017] [11]Detection of Malaria Parasites Using Digital Image Processing [2017] [12] Evaluations of Deep CNN for Automatic Identification of Malaria Infected Cells [2017] [13] Malaria Parasite Detection and Species Identification on Thin Blood Smears using a CNN [2017] [14] Malaria Parasite Detection from Peripheral Blood Smear Images using Deep Belief Networks [2017] [15] The Malaria System MicroApp: A New, Mobile Device-Based Tool for Malaria Diagnosis [2017] [16] Deep Residual Learning for Image Recognition [17] https://lhncbc.nlm.nih.gov/publication/pub9932 [18] https://www.pyimagesearch.com/2018/12/03/deep-learning-and-medical-image-analysis- with-keras/ [19] World Malaria Report 2017, WHO [20] Malaria Counting Protocol, WHO	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/814
dc.description	Supervised by Mr. Rafsanjany Kushol, Department of Computer Science and Engineering (CSE), Islamic University of Technology (IUT), Dhaka, Bangladesh.	en_US
dc.description.abstract	In the recent years, many researches have evolved in the field of CAD particularly in malaria diagnosis. Malaria diagnosis encompasses many domains, such as labelling of blood samples, detection of infected cells and furthermore determining the development stage and parasite type. Various techniques were implemented, among them, Deep learning has recently caught the attention of researchers. In this research project, we have implement ResNet-50, a variant of Deep Residual Learning to detect the presence of malaria on an erythrocyte. For this experiment, a dataset of 27,560 Red Blood Cells from NIH has been used. Samples were equally distributed. The Model achieved a training, validation and testing accuracy respectively of 96.50%, 96.78% and 97%. The whole process took 1 hour, and training-validation lasted for 50 epochs.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering, Islamic University of Technology, Board Bazar, Gazipur, Bangladesh	en_US
dc.title	Detection of Malaria on an Erythrocyte Using Deep Residual Network	en_US
dc.type	Thesis	en_US