Deep Learning for Malaria Detection

Use case in Healthcare Intelligence

Machine Learning and Deep Learning models combined with easy to build open source techniques can help improve the diagnosis of the life-threatening Malaria disease.

The objective of this usecase is to develop a model that can predict the probability of a human cell to be infected with Malaria parasite from an exploratory data analysis performed on a vast dataset containing images of infected and uninfected human cells. We leveraged deep learning models like CNN because of its effectiveness in providing solutions related to Computer Vision tasks. Using a CNN model we have been successful to predict both the categories and validate our approach to the future unseen data. 

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