Deep Learning Based Classification of Malaria from Slide Images

As one of the most life-threatening disease in the tropical and warmer-climate countries, Malaria affects not only animals but also humans who can be infected by only a single bite from a mosquito. Although this disease is wiped out in high-income countries, as a result of traveling people, it can even emerge in all part of the world. World Health Organization announced that more than 400,000 people are expected to die due to this illness. However, it is a curable and preventable disease, if early detection is possible. Traditionally, Pathologists diagnosed this disease manually by using microscope which is a time-consuming process in our computerized world, and this model depends on the experience of the Pathologists, which is a critical problem in rural areas. Therefore, in recent years detection of Malaria using computerized image analysis which is trained using some dynamic learning mechanism has gained increasing importance. In this paper, we proposed an image processing-based Malaria detection system which is trained by deep learning. We used relatively big data for increasing the accuracy of the system, and the reached accuracy showed that the proposed system has an outstanding classification rate that can be used in real-world detection.