ABSTRACT
Malaria is an ancient disease present majorly in the tropical countries having a huge social, economic, and health burden. In recent times, as a result of climatic changes due to global warming, it is predicted to have unexpected effects on Malaria. Both increase and fluctuation in temperature affects the vector and parasite life cycle. An efficient diagnostics is essential for the proper medication and cure. Major clinical diagnostics to identify RBCs affected by malaria is based on microscopic inspection of blood smears, treated with reagents, which stains the malarial parasite. Malaria is one of the most common diseases caused by mosquitoes and is a great public health problem worldwide. Currently, for malaria diagnosis the standard technique is microscopic examination of a stained blood film. We propose use of Neural Networks for the diagnosis of the disease in the blood cell. For this purpose features parameters are computed from the data obtained by the digital holographic images of the blood cells and is given as input which classifies the cell as the infected one or not. This project is developed in matlab using deep learning Convolutional Neural Network.
PROJECT OUTPUT
PROJECT DEMO VIDEO
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