In this blog we will talk about why Malaria detection is important to detect early presence of parasitized cells in a thin blood smear.
Introduction
Malaria is a deadly, infectious mosquito-borne disease caused by Plasmodium parasites. These parasites are transmitted by the bites of infected female Anopheles mosquitoes. While we won’t get into details about the disease, there are five main types of malaria.
Let’s now look at the significance of how deadly this disease can be in the following plot.
It is pretty clear that malaria is prevalent across the world particularly in tropical regions. The motivation for this project is but supported the character and fatality of this sickness. initially if associate infected mosquito bites you, parasites carried by the mosquito can get in your blood and begin destroying oxygen-carrying RBCs (red blood cells). Usually the primary symptoms of malaria are kind of like the flu or a virus once you usually start feeling sick within a few days or weeks once the mosquito bite. however these deadly parasites will sleep in your body for over a year with none problems! so, a delay in the right treatment will cause complications and even death. therefore early and effective testing and detection of malaria will save lives.
Approach to the solution
Although the malaria virus doesn’t take the form of a mutant mosquito, it sure feels like a mutant problem. The deadly disease has reached epidemic, even endemic proportions in different parts of the world — killing around 400,000 people annually . In other areas of the world, it’s virtually nonexistent. Some areas are just particularly prone to a disease outbreak — there are certain factors that make an area more likely to be infected by malaria .
- High poverty levels
- Lack of access to proper healthcare
- Political instability
- Presence of disease transmission vectors (ex. mosquitos) [6]
With this mixture of these problems, we must keep some things in mind when building our model:
- There may be a lack of a reliable power source
- Battery-powered devices have less computational power
- There may be a lack of Internet connection (so training/storing on the cloud may be hard!)
Traditional Methods for Malaria Detection
There are several methods and tests which can be used for malaria detection and diagnosis.
These include but are not limited to, thick and thin blood smear examinations, polymerase chain reaction (PCR) and rapid diagnostic tests (RDT). I will not going to cover all the methods but the thing is , traditional tests typically used an alternative particularly where good quality microscopy services cannot be readily provided.
Microscopic examination of blood is the best known method for diagnosis of malaria. A patient’s blood is smeared on a glass slide and stained with a contrasting agent that facilitates identification of parasites within red blood cells.
A trained clinician examines 20 microscopic fields of view at 100 X magnification, counting red blood cells that contain the parasite out of 5,000 cells (WHO protocol).
thanks Carlos Atico for this wonder blog on data science insights
Thus, malaria detection is definitely an intensive manual process, which can perhaps be automated using deep learning which forms the basis of this blog.
Deep learning for Malaria Detection
Deep Learning models, or if I have to say more specifically, Convolutional Neural Networks (CNNs) have proven to be really effective in a wide variety of computer vision tasks. While we assume that you have some knowledge on CNNs, in case you don’t, feel free to dive deeper into them by checking out this article here. Briefly, The key layers in a CNN model include convolution and pooling layers as depicted in the following figure.
Convolutional neural networks(CNN) can automatically extract features and learn filters. In previous machine learning solutions, features had to be manually programmed in — for example, size, color, the morphology of the cells. Utilizing Convolutional neural networks (CNN) will greatly speed up prediction time while mirroring (or even exceeding) the accuracy of clinicians.
CNN learns hierarchical patterns from our data. Thus they are able to learn different aspects of images. For example, the first convolution layer will learn small and local patterns such as edges and corners, a second convolution layer will learn larger patterns based on the features from the first layers, and so on.
You can go through an very interesting Research paper ‘Pre-trained convolutional neural networks as feature extractors toward improved parasite detection in thin blood smear images’ by Rajaraman et al. It explains a six pre-pretrained models on the data mentioned in the above paper. to obtain an accuracy of 95.9% in detecting malaria vs non-infected samples.
Dataset Explanation
Lets see what data we are using for this problem set , I am very thankful to researchers at the Lister Hill National Center for Biomedical Communications (LHNCBC), part of National Library of Medicine (NLM) who have carefully collected and annotated this dataset of healthy and infected blood smear images. You can download these images from the official site.
They had also launched an mobile application , that can run on an andriod smartphone attached to a conventional light microscope (Poostchi et al., 2018). Giemsa-stained thin blood smear slides from 150 P. falciparum-infected and 50 healthy patients were collected and photographed at Chittagong Medical College Hospital, Bangladesh. The smartphone’s built-in camera acquired images of slides for each microscopic field of view. The images were manually annotated by an expert slide reader at the Mahidol-Oxford Tropical Medicine Research Unit in Bangkok, Thailand.
So you can download the dataset and In the very next blog , we are going to see all insights of our dataset .
Moreover we are going to make an simple CNN model from scratch using open-source tools and frameworks which include Python and TensorFlow to build our models.
Happy learning and stay tuned!!!! 🙂
References
[1]: World Health Organization, Fact Sheet: World Malaria Report 2016, https://www.who.int/malaria/media/world-malaria-report-2016/en/ (13 December 2016).
[2] World Health Organization, Malaria, https://www.who.int/news-room/fact-sheets/detail/malaria (19 November 2018).
[3]: Carlos Atico Ariza, Malaria Hero: A web app for faster malaria diagnosis https://blog.insightdatascience.com/https-blog-insightdatascience-com-malaria-hero-a47d3d5fc4bb (Nov 6, 2018)
[4]: Rajaraman et al., Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images (2018). PeerJ 6:e4568; DOI 10.7717/peerj.4568
