• Miss. Kalyani Sakhare
• Prof.Dr. A. S. Joshi

Malaria is the deadliest disease in the earth and big hectic work for the health department. The traditional way of diagnosing malaria is by schematic examining blood smears of human beings for parasite-infected red blood cells under the microscope by lab or qualified technicians. This process is inefficient and the diagnosis depends on the experience and well knowledgeable person needed for the examination. Deep Learning algorithms have been applied to malaria blood smears for diagnosis before. Design propose system a new and highly robust deep learning model based on a convolutional neural network (CNN) which automatically classifies and predicts infected cells in thin blood smears on standard microscope slides. Testing on a small dataset of images gathered from a different source achieves similar performance, suggesting the model may generalize to different imaging conditions. The system achieves higher recall than existing non-deep approaches, and its accuracy, recall and precision of the highest performing CNN approach.

CNN,Deep Learn,Maleria

"Review On Image processing Based Automatic Detection Of Malaria", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 6, page no.e591-e595, June-2021, Available :http://www.jetir.org/papers/JETIR2106640.pdf

"Review On Image processing Based Automatic Detection Of Malaria", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 6, page no. ppe591-e595, June-2021, Available at : http://www.jetir.org/papers/JETIR2106640.pdf