Neural network based non‐invasive method to detect anemia from images of eye conjunctiva

Abstract

Detection of anemia can be done by examining the hemoglobin concentration level in the blood using complete blood count, which is an invasive, time‐consuming, and costly technique. Preliminary methods for detecting anemia include examining the color of the palpebral conjunctiva, which is a non‐invasive method, but color perception may vary from person to person. This study aims to develop a computerized non‐invasive technique for anemia detection. We propose a novel machine learning model using the artificial neural network to detect anemic patients from the images of eye conjunctiva. Since limited and small dataset has been used in the earlier approaches, this may cause over fitting of the model. We have improved the number of available training images using image augmentation techniques. To standardize a non‐invasive method, we have used computer vision algorithms for preprocessing and feature extraction. This article derives the backpropagation rules mathematically for adjusting the weights for the proposed neural network model. After hyper parameter tuning and using the mathematically derived backpropagation rules, the model was able to achieve the best accuracy of 97.00% with sensitivity 99.21% and specificity 95.42% on the created dataset.

Enhancing Intelligent Anemia Detection via Unifying Global and Local Views of Conjunctiva Image with Two-Branch Neural Networks

“…Such approach aims at training deep learning models with a set of annotated images, which would give automatic diagnosis without any intervention of professionals. [11] Developed a deep learning approach which uses fundus retinal images plus patients' metadata to perform automated anemia screening. [16] constructed neural network models for anemia detection using conjunctiva images.…”

“…[11] Developed a deep learning approach which uses fundus retinal images plus patients' metadata to perform automated anemia screening. [16] constructed neural network models for anemia detection using conjunctiva images. [17] proposed a U-Net based conjunctiva segmentation model to detecting anemia.…”

“…In this paper, we focus on developing more effective algorithms for one of such approaches, that is, to detect anemia with photos of conjunctiva [10][11][12]. Comparing to other similar approaches like diagnosis with retina images, detecting with conjunctiva images requires no extra professional equipment, which brings much convenience to users.…”

Smartphone-based photo analysis for the evaluation of anemia, jaundice and COVID-19

“…To provide quantitative results, the smartphone's camera can be used to take pictures of these regions for further analysis. 10 Jain et al 11 used an artificial neural network to detect anemia from photos of the palpebral conjunctiva of 48 anemic patients and 51 non-anemic patients. When there are few data samples available, it is necessary to implement data augmentation strategies to produce new artificial data from the original.…”

“…The three layers are: an input layer (red and green components), a hidden layer, and an output layer, consisting of interconnected neurons that allow neural networks to learn patterns. 11 A smartphone application (Selfienemia) was developed to estimate hemoglobin levels under controlled lighting conditions. After capturing and processing the photo, a colorimetric analysis is performed using a mathematical model from a smartphone cloud service.…”