Rsheikh-shab/Fight-against-malaria-epidemics-in-Africa

The purpose of this project is to build a detection system for malaria parasites in images. This would avoid some of the current obstacles of malaria diagnosis such as the lack of expert technicians in low-resource areas. In contrast to most of the previous work we use thick blood smears instead of thin smears. These are the preferred ones for diagnosis, although they make the task harder. We use Convolutional Neural Networks, a particular type of deep neural network, to classify individual patches as containing parasites or not. Then, we use computer vision techniques such as non-maxima suppression to integrate the results and locate each parasite. The results are compared to those obtained with Extremely Randomized Trees and hand-designed features as used in previous work on this dataset (Quinn et al, 2014 [1]). Our results are notably better than the models proposed previously. For a recall of 90% we have obtained a gain in the precision from 37% to 78%, which is enough to already improve the work conditions of laboratory technicians working on malaria diagnosis. Furthermore, the system can be easily extended to the detection of other parasites or blood components without much effort, so several tests may be run at the same time on the blood smears. # Fight-against-malaria-epidemics-in-Africa The purpose of this project is to build a detection system for malaria parasites in images. This would avoid some of the current obstacles of malaria diagnosis such as the lack of expert technicians in low-resource areas. In contrast to most of the previous work we use thick blood smears instead of thin smears. These are the preferred ones for diagnosis, although they make the task harder. We use Convolutional Neural Networks, a particular type of deep neural network, to classify individual patches as containing parasites or not. Then, we use computer vision techniques such as non-maxima suppression to integrate the results and locate each parasite. The results are compared to those obtained with Extremely Randomized Trees and hand-designed features as used in previous work on this dataset (Quinn et al, 2014 [1]). Our results are notably better than the models proposed previously. For a recall of 90% we have obtained a gain in the precision from 37% to 78%, which is enough to already improve the work conditions of laboratory technicians working on malaria diagnosis. Furthermore, the system can be easily extended to the detection of other parasites or blood components without much effort, so several tests may be run at the same time on the blood smears.