Plasmodium Parasite Detection Using Combination of Image Processing and Deep Learning Approach

[1]  Esra Var,et al.  Malaria Parasite Detection with Deep Transfer Learning , 2018, 2018 3rd International Conference on Computer Science and Engineering (UBMK).

[2]  Yuhang Dong,et al.  Evaluations of deep convolutional neural networks for automatic identification of malaria infected cells , 2017, 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI).

[3]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[4]  Yuhang Dong,et al.  Classification accuracies of malaria infected cells using deep convolutional neural networks based on decompressed images , 2017, SoutheastCon 2017.

[5]  Keerthana Prasad,et al.  Development of a Robust Algorithm for Detection of Nuclei and Classification of White Blood Cells in Peripheral Blood Smear Images , 2018, Journal of Medical Systems.

[6]  Febri Maspiyanti,et al.  Plasmodium Parasite Detection on Thin Blood Smear Image using Double Thresholding and BLOB Analysis , 2018, 2018 International Conference on Applied Engineering (ICAE).

[7]  Wongwit Senavongse,et al.  Automated detection of plasmodium falciparum from Giemsa-stained thin blood films , 2016, 2016 8th International Conference on Knowledge and Smart Technology (KST).

[8]  Madhu S. Nair,et al.  Malaria Parasite Detection From Peripheral Blood Smear Images Using Deep Belief Networks , 2017, IEEE Access.

[9]  Igi Ardiyanto,et al.  Enumeration of Plasmodium Parasites on Thin Blood Smear Digital Microscopic Images , 2019, 2019 5th International Conference on Science in Information Technology (ICSITech).

[10]  Adhistya Erna Permanasari,et al.  Automated determination of Plasmodium region of interest on thin blood smear images , 2017, 2017 International Seminar on Intelligent Technology and Its Applications (ISITIA).

[11]  Prospero C. Naval,et al.  Malaria Parasite Detection and Species Identification on Thin Blood Smears Using a Convolutional Neural Network , 2017, 2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE).

[12]  M. Y. Mashor,et al.  A fast and accurate detection of Schizont plasmodium falciparum using channel color space segmentation method , 2017, 2017 5th International Conference on Cyber and IT Service Management (CITSM).

[13]  Hanung Adi Nugroho,et al.  Plasmodium Candidate Detection on Thin Blood Smear Images with Luminance Noise Reduction , 2019, 2019 5th International Conference on Science in Information Technology (ICSITech).