Point of care detection of anemia in non-invasive manner by using image processing and convolutional neural network with mobile devices
Abstract
A medical procedure is described as non-invasive if there is no necessity
for incision into the skin. Invasive methods can often be risky because
there is a high chance of infection. That is why the medical sector is
constantly trying to shift its momentum into non-invasive methods. The
traditional method of detecting Anemia is by testing blood samples taken
from the patient’s body. Anemia is a medical condition when the human
body suffers from a lack of RBC. RBC is the carrier of oxygen to the
blood. If not checked regularly, this may lead to kidney failure or even
premature births. The situation gets worse in the rural areas of developing nations, where medical equipment is often unavailable. This study
proposes a non-invasive point of care solution using images captured from
mobile phone videos. Image processing is a vastly used technique around
the world. Mostly because it allows various algorithms to be applied and
reduces the build-up of noise and deformation during image processing.
Usually, an Anemic patient’s blood contains less hemoglobin. Anemic
blood transmits more light compared to normal blood, so the severity
of anemia can be measured by analyzing the color of blood. Using the
flashlight, the capturing of blood color was made possible without drawing blood. Hence, this paper tried to analyze the sample using python
image processing that will enable the chances of a low-cost point of care
solution for the detection of Anemia.