Deep Learning Based Automatic Malaria Parasite Detection from Blood Smear and its Smartphone Based Application.

ABSTRACT: Malaria is a life-threatening disease that is spread by the Plasmodium parasites. It is detected by trained microscopists who analyze microscopic blood smear images. Modern deep learning techniques may be used to do this analysis automatically. The need for the trained personnel can be greatly reduced with the development of an automatic accurate and efficient model. In this article, we propose an entirely automated Convolutional Neural Network (CNN) based model for the diagnosis of malaria from the microscopic blood smear images. A variety of techniques including knowledge distillation, data augmentation, Autoencoder, feature extraction by a CNN model and classified by Support Vector Machine (SVM) or K-Nearest Neighbors (KNN) are performed under three training procedures named general<

SUBMITTER: Fuhad KMF 

PROVIDER: S-EPMC7277980 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature