• Chhaya Vankhede

This study has been done to make the diagnosis of malaria easy, faster and less expensive. Conventional microscopy is one of the best ways for diagnosis of the disease. These techniques including other techniques are time-consuming, expensive and require expert pathologists. Thus in this work semi-automatic malaria diagnosis system is developed which uses stained thin blood smear images. We have developed this kind of system using Convolutional Neural Network(CNN), which is trained and tested using 20000 images of the stained blood smear. This system provides 96% accuracy on test data.

Malaria Diagnosis, Machine Learning, CNN, Image classification, Accuracy, Parasite.

" MALARIA DIAGNOSIS USING CONVOLUTIONAL NEURAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.98-103, April-2019, Available :http://www.jetir.org/papers/JETIRAX06020.pdf

" MALARIA DIAGNOSIS USING CONVOLUTIONAL NEURAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp98-103, April-2019, Available at : http://www.jetir.org/papers/JETIRAX06020.pdf