scholarly journals Malaria Parasite Detection from Microscopic Blood Smear Image: A Literature Survey

2019 ◽  
Vol 182 (48) ◽  
pp. 61-66
Author(s):  
Thenu Eliza ◽  
Sreekumar K.
Keyword(s):  
Malaria Parasite ◽  
Blood Smear ◽  
Literature Survey ◽  
Parasite Detection ◽  
Blood Smear Image

Blood Smear Image Based Malaria Parasite and Infected-Erythrocyte Detection and Segmentation

2015 ◽  
Vol 39 (10) ◽  
Author(s):  
Meng-Hsiun Tsai ◽  
Shyr-Shen Yu ◽  
Yung-Kuan Chan ◽  
Chun-Chu Jen
Keyword(s):  
Malaria Parasite ◽  
Blood Smear ◽  
Infected Erythrocyte ◽  
Blood Smear Image

scholarly journals Malaria parasite detection and cell counting for human and mouse using thin blood smear microscopy

2018 ◽  
Vol 5 (04) ◽  
pp. 1 ◽  
Author(s):  
Mahdieh Poostchi ◽  
Ilker Ersoy ◽  
Katie McMenamin ◽  
Emile Gordon ◽  
Nila Palaniappan ◽  
...  
Keyword(s):  
Malaria Parasite ◽  
Blood Smear ◽  
Cell Counting ◽  
Smear Microscopy ◽  
Thin Blood Smear ◽  
Parasite Detection ◽  
Human And Mouse

scholarly journals Lightweight Deep Learning for Malaria Parasite Detection Using Cell-Image of Blood Smear Images

2020 ◽  
Vol 34 (5) ◽  
pp. 571-576
Author(s):  
Amin Alqudah ◽  
Ali Mohammad Alqudah ◽  
Shoroq Qazan
Keyword(s):  
Blood Cells ◽  
Malaria Parasite ◽  
Blood Smear ◽  
Machine Learning Algorithms ◽  
Infected Female ◽  
Plasmodium Parasite ◽  
Cell Image ◽  
Input Size ◽  
Parasite Detection ◽  
Global Population

Malaria is an infectious disease that is caused by the plasmodium parasite which is a single-celled group. This disease is usually spread employing an infected female anopheles mosquito. Recent statistics show that in 2017 there were only around 219 million recorded cases and about 435,000 deaths were reported due to this disease and more than 40% of the global population is at risk. Despite this, many image processing fused with machine learning algorithms were developed by researchers for the early detection of malaria using blood smear images. This research used a new CNN model using transfer learning for classifying segmented infected and Uninfected red blood cells. The experimental results show that the proposed architecture success to detect malaria with an accuracy of 98.85%, sensitivity of 98.79%, and a specificity of 98.90% with the highest speed and smallest input size among all previously used CNN models.

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