Detection of Malaria in Blood Cells Using Convolution Neural Network

There are a variety of automated diagnostic techniques and models using numerous supervised learning models, but most of these models cater especially to the diseases that are seen in the Western countries, and they rarely see the outbreak of diseases such as malaria, dengue, etc. Early detection of these diseases can control the mortality rate and help save lives. Malaria while being a curable disease still has no vaccine available for it, so early detection of malaria can help determine the risk and can prove to be lifesaving. And with the time taken to collect, analyse and diagnose malaria in the blood is valuable time that can be the difference between a patient’s life or death. And less developed countries do not have the proper resources for fast response against the disease. In this project, we are hoping to develop an effective and efficient automated diagnostic model using machine learning models. For this, we have implemented a model based on CNN architecture to detect malarial parasites in blood cells and then use advance image processing techniques to contour and isolate the parasite to track the progression of the disease.