An Automatic Malaria Disease Diagnosis Framework Integrating Blockchain-Enabled Cloud–Edge Computing and Deep Learning

Malaria is a life-threatening disease, which mainly occurs in developing countries and regions with poor sanitary conditions. Early diagnosis of malaria will effectively decrease the death rate. In this article, we develop an automatic malaria disease diagnosis framework integrating blockchain-enabled cloud–edge computing and deep learning. The diagnosis task is divided into malaria parasite segmentation from blood smear images and classification of parasite species and stages. To meet the massive demand for deep learning training, we design a diagnosis pipeline that is deployed in a cloud–edge paradigm to utilize both local and remote resources. At edge nodes, preprocessed data sets are classified by U-Net in a supervised approach to generate coarse probability maps. Then, the normalized images and generated probability maps are uploaded to the cloud server. At the cloud, the uploaded probability maps are used to weakly supervise the stacked dilated U-Net (SDU-Net) to segment infected cells. Further classifications of malaria parasites species and stages are conducted by a pretrained MobileNet V1. The blockchain technology is adopted during the data transmission process. The diagnosis results will be sent back to the original local hospital immediately through the cloud. Our framework improves the diagnosis accuracy and eases the burden of deep learning training. Evaluation on real data collection MP-IDB demonstrated the effectiveness of our method.