Detecting Anemia Based on Palm Images using Convolutional Neural Network

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.