| Popis: |
With 200 million cases annually, malaria often claims more lives than crisis and deadly wars. Given the ineffectiveness of efforts to lower mortality rates, insufficient malaria diagnosis is one of the hurdles to a successful and efficient reduction in fatality. Hence, malaria is one of the major causes of deaths and diseases in many developing countries, where young children and prenatal mothers are the most impacted populations. The parasite called Plasmodium is the source for the potentially fatal disease named malaria. Highly trained and experienced microscopists observe minute blood smeared images to look for the parasite. Modern deep learning techniques could automate the completion of the required analysis. With the development of an independent, accurate, and useful model the demand for skilled staff can be significantly decreased. In this study, we offer a totally automated method and approach for the diagnosis and classification of malaria using microscopic blood-smeared pictures based on convolutional neural networks-(CNN). |
