Smartphone-Supported Malaria Diagnosis Based on Deep Learning

Abstract

Malaria remains a major burden on global health, causing about half a million deaths every year. The objective of this work is to develop a fast, automated, smartphone-supported malaria diagnostic system. Our proposed system is the first system using both image processing and deep learning methods on a smartphone to detect malaria parasites in thick blood smears. The underlying detection algorithm is based on an iterative method for parasite candidate screening and a convolutional neural network model (CNN) for feature extraction and classification. The system runs on Android phones and can process blood smear images taken by the smartphone camera when attached to the eyepiece of a microscope. We tested the system on 50 normal patients and 150 abnormal patients. The accuracies of the system on patch-level and patient-level are 97% and 78%, respectively. AUC values on patch-level and patient-level are, respectively, 98% and 85%. Our system could aid in malaria diagnosis in resource-limited regions, without depending on extensive diagnostic expertise or expensive diagnostic equipment.