Abstract

To create an automatic data annotation tool and ground truth dataset for malaria diagnosis using deep learning. The ground truth dataset and the tool will streamline the development of AI tools for pathology diagnosis.

Introduction

Technology is transforming how health care is delivered in Africa, providing more people especially in limited resource setting areas and around the world access to better care. Likewise, easier access to data supports both doctors and policymakers in making better-informed decisions about how to continue to improve the health care system. However, the existing traditional methods especially for disease diagnosis have limitations such as expensive equipment, need of experts and time consuming for a single diagnosis. This becomes impractical in areas with high disease burden such as sub-Saharan regions. In this project we focus on improvement of malaria diagnosis.We choose malaria because it is a life threatening disease dominant in developing countries. According to WHO In 2017, nearly half of the world’s population was at risk of malaria with more than 90 countries reporting malaria cases and Africa was home to 435,000 death. Also they report that malaria kills a child every 2 minutes. Nevertheless, prompt diagnosis and treatment can reduce such death.

In the area of Artificial Intelligence (AI), several techniques have been adopted to create malaria diagnosis tools that are fast, accurate and requires less experts. Deep convolutional networks as one of AI techniques, has been used for detection of malaria parasites (Sanchez Sanchez, 2015). Concerning the sensitivity of health, AI tools dealing with health issues such as diagnosis usually require large amounts of data in order to achieve high accuracy for its applicability. However, in the context of developing countries there is a shortage of such data for research and developing such tools. Henceforth, there is a necessity of creation of dataset for research and development of pathology diagnosis tool such as for malaria.

Rationale

One of the major problems that hinders development of AI and its applicability in developing countries include lack of data. This is evident in limited access to the available data from both government and non-governmental organization. In addition to that, data may be available but still lacks the necessary quality in terms of pixels, labels that is required for development of AI tools. Lastly, in some scenarios such as agriculture and health there is no data to be used for training, testing and validation of AI tools. For these reasons, it becomes difficult and takes a longer time to create a comprehensive dataset. These problems regarding dataset, particularly in the health sector, cause a significant setback to the AI tools development which is a potential technology in solving problems in our health sector. Therefore, there is a need of  coming up with a tool for improving the entire process of acquiring dataset.

Main Objective

The aim of this project is to create AI tool that will be used to effectively create ground truth dataset for malaria diagnosis using deep learning. Specific Objectives:

• To capture microscopic images of malaria parasitized and uninfected stained blood smear
  sample using a smartphone.
• To develop automatic annotation tool for the captured images by integrating an open
  source annotation tool and object detection model.
• To verify the effectiveness of the automatic annotation tool.