• Abdul Sameer
• Pushya Ragini Koppula
• Dr Devikanniga

Malaria is an infectious disease which is caused by plasmodium parasites. Several image processing and deep learning techniques have been employed to diagnose malaria, using its spatial features extracted from microscopic images. In this work, a model and a technique are introduced for identifying infected falciparum malaria parasites using a transfer learning approach and foldscope (Origami-Based Paper Microscope). transfer learning approach can be achieved by unifying the existing pre-trained model. A malaria digital corpus generated by acquiring blood smear images of infected and non-infected malaria patients and obtaining the result which shows the potential of transfer learning in the field of malaria diagnosis. and further testing on the images taken from mobile using foldscope.

cell-phone camera, Deep learning, foldscope, Malaria, Transfer learning, ResNet-50, SoftMax

"USING DEEP LEARNING, FOLDSCOPE AND MOBILE BASED APPLICATION FOR DETECTING MALARIA PARASITE ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 8, page no.1931-1935, August-2020, Available :http://www.jetir.org/papers/JETIR2008255.pdf

"USING DEEP LEARNING, FOLDSCOPE AND MOBILE BASED APPLICATION FOR DETECTING MALARIA PARASITE ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 8, page no. pp1931-1935, August-2020, Available at : http://www.jetir.org/papers/JETIR2008255.pdf