Reducing data dimension boosts neural network-based stage-specific malaria detection

[1]  Dada Emmanuel Gbenga,et al.  A Novel Data Augmentation Convolutional Neural Network for Detecting Malaria Parasite in Blood Smear Images , 2022, Appl. Artif. Intell..

[2]  Valentina E. Garcia,et al.  Label-free imaging and classification of live P. falciparum enables high performance parasitemia quantification without fixation or staining , 2021, PLoS Comput. Biol..

[3]  B. Vértessy,et al.  Stage-Dependent Topographical and Optical Properties of Plasmodium Falciparum-Infected Red Blood Cells , 2021 .

[4]  Mohsen Ali,et al.  A dataset and benchmark for malaria life-cycle classification in thin blood smear images , 2021, Neural Computing and Applications.

[5]  Danny W. Wilson,et al.  Automated detection and staging of malaria parasites from cytological smears using convolutional neural networks , 2021, Biological Imaging.

[6]  T. Dijkstra,et al.  Detection and stage classification of Plasmodium falciparum from images of Giemsa stained thin blood films using random forest classifiers , 2020, Diagnostic Pathology.

[7]  Krit Sriporn,et al.  Analyzing Malaria Disease Using Effective Deep Learning Approach , 2020, Diagnostics.

[8]  Nabeel Mohammed,et al.  Deep Learning Based Automatic Malaria Parasite Detection from Blood Smear and Its Smartphone Based Application , 2020, Diagnostics.

[9]  Sameer Antani,et al.  Deep Learning for Smartphone-Based Malaria Parasite Detection in Thick Blood Smears , 2020, IEEE Journal of Biomedical and Health Informatics.

[10]  I. Sherman,et al.  Plasmodium , 2020, Definitions.

[11]  Mohammad Sohel Rahman,et al.  Improving Malaria Parasite Detection from Red Blood Cell using Deep Convolutional Neural Networks , 2019, ArXiv.

[12]  Taghi M. Khoshgoftaar,et al.  A survey on Image Data Augmentation for Deep Learning , 2019, Journal of Big Data.

[13]  Sameer K. Antani,et al.  Performance evaluation of deep neural ensembles toward malaria parasite detection in thin-blood smear images , 2019, PeerJ.

[14]  Mayoore S. Jaiswal,et al.  Automated microscopy for routine malaria diagnosis: a field comparison on Giemsa-stained blood films in Peru , 2018, Malaria Journal.

[15]  Mahdieh Poostchi,et al.  Image analysis and machine learning for detecting malaria , 2018, Translational research : the journal of laboratory and clinical medicine.

[16]  Dzulkifli Mohamad,et al.  Machine aided malaria parasitemia detection in Giemsa-stained thin blood smears , 2018, Neural Computing and Applications.

[17]  M. Batty,et al.  Multispectral Atomic Force Microscopy-Infrared Nano-Imaging of Malaria Infected Red Blood Cells. , 2018, Analytical chemistry.

[18]  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).

[19]  J. Cunningham,et al.  Quality issues with malaria rapid diagnostic test accessories and buffer packaging: findings from a 5-country private sector project in Africa , 2017, Malaria Journal.

[20]  Mulugeta Russom,et al.  Rapid diagnostic tests failing to detect Plasmodium falciparum infections in Eritrea: an investigation of reported false negative RDT results , 2017, Malaria Journal.

[21]  Kishor P. Upla,et al.  Computer aided diagnosis of Malaria disease for thin and thick blood smear microscopic images , 2017, 2017 4th International Conference on Signal Processing and Integrated Networks (SPIN).

[22]  T. Limpiti,et al.  Automated classification of malaria parasite species on thick blood film using support vector machine , 2015, 2015 8th Biomedical Engineering International Conference (BMEiCON).

[23]  M. Rebelo,et al.  Efficient monitoring of the blood-stage infection in a malaria rodent model by the rotating-crystal magneto-optical method , 2015, Scientific Reports.

[24]  F. Gołek,et al.  AFM image artifacts , 2014 .

[25]  Tania S. Douglas,et al.  Automated detection of malaria in Giemsa-stained thin blood smears , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[26]  T. Abbasian,et al.  Automatic Malaria Diagnosis system , 2013, 2013 First RSI/ISM International Conference on Robotics and Mechatronics (ICRoM).

[27]  Kevin S. W. Tan,et al.  Life Cycle-Dependent Cytoskeletal Modifications in Plasmodium falciparum Infected Erythrocytes , 2013, PloS one.

[28]  Izzet Kale,et al.  Automated malaria parasite detection in thin blood films:- A hybrid illumination and color constancy insensitive, morphological approach , 2012, 2012 IEEE Asia Pacific Conference on Circuits and Systems.

[29]  Chandan Chakraborty,et al.  Probabilistic prediction of malaria using morphological and textural information , 2011, 2011 International Conference on Image Information Processing.

[30]  T. Gilberger,et al.  Development and host cell modifications of Plasmodium falciparum blood stages in four dimensions. , 2011, Nature communications.

[31]  Raghuveer M. Rao,et al.  Segmentation of malaria parasites in peripheral blood smear images , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[32]  John Heptinstall,et al.  A magneto-optic route toward the in vivo diagnosis of malaria: preliminary results and preclinical trial data. , 2008, Biophysical journal.

[33]  F. Schuster,et al.  Cultivation of Plasmodium spp , 2002, Clinical Microbiology Reviews.

[34]  H Pertoft,et al.  Fractionation of cells and subcellular particles with Percoll. , 2000, Journal of biochemical and biophysical methods.

[35]  J. Dvorak,et al.  Plasmodium falciparum-infected erythrocytes: qualitative and quantitative analyses of parasite-induced knobs by atomic force microscopy. , 2000, Journal of structural biology.

[36]  M. Wasserman,et al.  Separation and concentration of schizonts of Plasmodium falciparum by Percoll gradients. , 1983, The Journal of protozoology.

[37]  C. Lambros,et al.  Synchronization of Plasmodium falciparum erythrocytic stages in culture. , 1979, The Journal of parasitology.

[38]  W. Trager,et al.  Human malaria parasites in continuous culture. , 1976, Science.

[39]  P.V.C. Hough,et al.  Machine Analysis of Bubble Chamber Pictures , 1959 .

[40]  M. Delves,et al.  Malaria Parasite Detection Using Deep Learning Methods , 2021 .

[41]  B. Vértessy,et al.  Supplementary Information on : Malaria pigment crystals as magnetic micro-rotors : key for high-sensitivity diagnosis , 2013 .

[42]  D. Sullivan,et al.  Hemoglobin metabolism in the malaria parasite Plasmodium falciparum. , 1997, Annual review of microbiology.

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