| dc.description.abstract | Hemoglobin is a protein in the blood that conveys oxygen from
the lungs to the body's tissues. Hemoglobin levels under the normal limit
cause anemia. Hemoglobin estimation is generally utilizing a needle to take
the patient’s blood as a sample and afterward testing it at the chemicals
laboratory. This technique has a shortcoming, specifically, it is less
proficient because it requires a few hours. Likewise, it needs to hurt the
patient's skin with a hypodermic needle. In this study, we will discuss the
Convolutional Neural Network (CNN) in classifying hemoglobin levels
based on palm images. Hemoglobin levels are partitioned into two classes,
to be anemia and non-anemia. The image size utilized is 500×375 pixels
with the number of Red, Green, and Blue (RGB) channels. The data utilized
in this study were images of the patient's palm. The first important phase in
this research was data retrieval, which went on with preprocessing data,
then the data is clustered into two clusters using a random state, then at
that point, each cluster will be classified using the CNN algorithm.
The best results are obtained by the value of accuracy reached 96.43%
with a precision score of 93.75% achieved, recall of 100%, and specificity
of 92.31% for cluster 1 in random state 1, and the similar random state for
cluster 2 is obtained the value of accuracy reached 96.43% with a
precision score of 93.33%, recall of 100%, and specificity of 92.86% were
achieved this way. | en_US |
