Design and implementation of a low-cost malaria diagnostic system based on convolutional neural network

This work focuses on the design and implementation of an intelligent system that can diagnose malaria from blood smear images. This system takes data in the image format and provides an instant and automated diagnosis to output the result of the patient’s condition on a screen. The methodology for achieving the system is based on the CNN (convolutional neural network). The latter has the specificity to function as a feature extractor and image classifier. The software part thus obtained is implemented in an electronic device that serves as a kit mounted with our care. The establishment of such a system has innumerable assets, such as rapidity during diagnosis by a laboratory technician or not; its portability that will facilitate its use wherever needed. From an ergonomic and functional point of view, the system has a real impact in the diagnosis of a large-scale malaria endemic. The CNN was trained on a large dataset of blood smears and was able to accurately classify infected and uninfected samples with high sensitivity and specificity. Insofar as the system carried out after testing on several samples reaches an average sensitivity of 89.50% and an average precision of 89%, this improves decision-making on the diagnosis of malaria. The system thus created allows malaria to be diagnosed at low cost from blood smear images. The use of CNNs in this project has the advantage of automatically extracting features from blood smear images and classifying them efficiently. The major advantage of the proposed system is its portability and lower cost. The performance of the proposed algorithm was evaluated on a publicly available malaria data set. • The image of the Giemsa-stained thin blood smear is taken by the embedded camera. • Uninfected red blood cells look as oval forms with a bright middle and sleek exterior. • Classification and feature extraction are performed by a convolutional neural network. • The system achieved an accuracy of 89% in classifying infected and uninfected cells.