Machine learning approach for automated screening of malaria parasite using light microscopic images-Reference-Cited by-同舟云学术

Machine learning approach for automated screening of malaria parasite using light microscopic images

Published:2013-02 Issue: Volume:45 Page:97-106
ISSN:0968-4328
Container-title:Micron
language:en
Short-container-title:Micron

Author:

Das Dev Kumar,Ghosh Madhumala,Pal Mallika,Maiti Asok K.,Chakraborty Chandan

Funder

Dept. of Information Technology, Govt. of India

Publisher

Elsevier BV

Subject

Cell Biology,Structural Biology,General Physics and Astronomy,General Materials Science

Reference41 articles.

1. The morphological approach to segmentation: the watershed transformation;Beucher,1993

2. Use of gray value distribution of run lengths for texture analysis;Chu;Pattern Recognition Letter,1990

3. Cuomo, M.J., Noel, L.B., White, D.B., 2012. Diagnosing Medical Parasites: A Public Health Officers Guide to Assisting Laboratory and Medical Officers. Retrieved from http://www.phsource.us/PH/PARA/Diagnosing_Medical_Parasites.pdf.

4. Invariant moment based feature analysis for abnormal erythrocyte recognition;Das,2010

5. Image characterization based on joint gray-level run-length distribution;Dasarathy;Pattern Recognition Letter,1991

Cited by 181 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Image cropping for malaria parasite detection on heterogeneous data;Journal of Microbiological Methods;2024-10

2. Computer-Aided Diagnosis Systems for Automatic Malaria Parasite Detection and Classification: A Systematic Review;Electronics;2024-08-11

3. Application of image recognition technology in pathological diagnosis of blood smears;Clinical and Experimental Medicine;2024-08-06

4. DCDLN: A densely connected convolutional dynamic learning network for malaria disease diagnosis;Neural Networks;2024-08

5. Majority-LCL: Towards Malaria Cell Detection using Label Contrastive Learning and Majority voting Ensembling;2024-07-23