Benchmarking Malaria Detection Algorithms

Malaria is one of the top 10 causes of death in the sub Saharan Africa. According to the World Health Organization report of 2016, of the 438,000 Malaria deaths registered, an estimated 92% of all Malaria cases resulted in deaths, two thirds of which occurred among children under five years of age. Referring also to records from WHO report of 2015, Malaria accounted for 480,000 deaths, 90% of which were from Africa, 7% from S.E Asia and 2% from Eastern Mediterranean region. Although there were fewer Malaria cases in 2017 than in 2010 according to WHO report of 2017, data for the period 2015–2017 highlighted that no significant progress in reducing global Malaria cases was made in this timeframe. Malaria is thus of major concern to public health.

Benchmark

Benchmarking of “AI-based detection of Malaria” aims at considering many aspects that concern the entire process of detection. These span from the data collection pipeline, the data, the analysis engine, the output, the evaluation measures, the test data set and the framework design. The topic will in future benchmark on the benefits from multiple data sets whose factors could potentially improve the detective index of the AI based detection model.

How to participate

Prerequisites: Install Anaconda Python 3.6.6, Tensorflow (2.1.0), opencv-python (4.0.1), scikit-image (0.15.0) Download the starting kit. Usage: - modify sample_code_submission/model.py to provide a better model - zip the contents of sample_code_submission (without the directory, but with metadata)