Malaria Parasite Detection with Deep Transfer Learning

Author(s):  
Esra Var ◽  
F. Boray Tek
Author(s):  
K Venkata Shiva Rama Krishna Reddy ◽  
◽  
S Phani Kumar ◽  

Malaria parasitized detection is very important to detect as there are so many deaths due to false detection of malaria in medical reports. So analysis has gained a lot of attention in recent years. Detection of malaria is important as fast as possible because detecting malaria is difficult in blood smears. Our idea is to build a transfer learning model and detect the thick blood smears whether the presence of malaria parasites in a drop of blood. The data consists of 5000 each infected and uninfected data obtained from the NIH website. In this paper, I propose to use three different types of neural networks for the performance evaluation of the malaria data by transfer learning using CNN, VGG19, and fine-tuned VGG19. Transfer learning model performed well among various other models by achieving a precision of 98 percent and an f-1 score of 96 percent.


2020 ◽  
Vol 24 (5) ◽  
pp. 1427-1438 ◽  
Author(s):  
Feng Yang ◽  
Mahdieh Poostchi ◽  
Hang Yu ◽  
Zhou Zhou ◽  
Kamolrat Silamut ◽  
...  

Author(s):  
Yuchen Li ◽  
Siming Zuo ◽  
Jacob Thompson ◽  
Lisa Ranford-Cartwright ◽  
Nosrat Mirzai ◽  
...  

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