Detection of anaemia using medical images: A comparative study of machine learning algorithms – A systematic literature review

Asare, Justice Williams; Appiahene, Peter; Donkoh, Emmanuel Timmy

doi:10.1016/j.imu.2023.101283

Cited by 11 publications

(4 citation statements)

References 38 publications

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“…Figure 13 represents the Naïve Bayes algorithm's confusion matrix. The calculation of probability for a target class is done using the following formula [16]:…”

Section: Naïve Bayesmentioning

confidence: 99%

Prediction of Anemia using various Ensemble Learning and Boosting Techniques

Schweta,

Pande

2023

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Anemia is a disease of great concern. It is mainly seen in people who are deficient in several vitamins like B12 and those who are deficient in iron. Neglecting the situation and leaving it untreated could lead to severe consequences in the future. Hence it is of great importance to predict Anemia in an individual and treat it in the optimum stage. OBJECTIVES: In this paper, machine learning was used for the prediction of Anemia. METHODS: The dataset used for this was formed by combining different datasets from Kaggle. The accuracy of various machine learning techniques was evaluated to find out the best one. Along with the supervised learning algorithms like Random Forest, SVM, Naive Bayes etc., Linear Discriminant Analysis, Quadratic Discriminant Analysis and ensemble learning methods were also performed. RESULTS: Upon evaluation, among the best performers, the execution time was also taken into consideration to determine which classifier works well. Among all the algorithms used, XGboost worked the best with an optimum execution time. CONCLUSION: The conclusion is that for the data used in the work, XGboost results as the best model.

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“…Figure 13 represents the Naïve Bayes algorithm's confusion matrix. The calculation of probability for a target class is done using the following formula [16]:…”

Section: Naïve Bayesmentioning

confidence: 99%

Prediction of Anemia using various Ensemble Learning and Boosting Techniques

Schweta,

Pande

2023

EAI Endorsed Trans Perv Health Tech

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“…Many Studies have been conducted to solve this problem. These studies mentioned that there are some non-invasive and easier way of detecting anaemia range from the eye's conjunctiva, the color of fingernail, the hand palms and also from the tongue [4] [5]. But among these, the most dominant and accurate is based on the conjunctiva analysis [6].…”

Section: Introductionmentioning

confidence: 99%

Automated Anaemia Detection From Conjunctiva Images : A Machine Learning Approach for Android Application

Henry,

Sumihar,

Maedjaja

2024

jipi. jurnal. ilmiah. penelitian. dan. pembelajaran. informatik

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Anaemia is a common global disorder condition in which the red blood cell count is lower than normal. Traditional diagnostic methods often prove costly, invasive, and inaccessible, leading to delays in treatment and severe consequences. This study explores non-invasive techniques leveraging smartphone technology for efficient anaemia detection. Some researcher investigated that eye’s conjunctiva analysis can a viable alternative, considering its rich blood vessel network and sensitivity to anaemia indicators, independent of skin color. Utilizing smartphone cameras, the study establishes a robust correlation between the color of the conjunctiva and anaemia status, offering a promising avenue for non-invasive diagnosis. Employing a hybrid methodology, the study integrates You Only Look Once (YOLO) version 8 for efficient object detection, achieving a mean average precision of 96% in extracting Regions of Interest (ROI) from conjunctiva images. Subsequently, K-Nearest Neighbors (KNN) classification of features extracted from these ROI’s attained an 83% accuracy rate in determining anaemia status. Furthermore, the study aims to extend its impact by developing an Android application using the Flutter framework. This application integrates the established YOLO and KNN approaches, enabling real-time anaemia detection through smartphone cameras. Such a tool holds the potential to facilitate early evaluations in resource-constrained regions, enabling timely diagnosis and intervention, thus mitigating the adverse effects of untreated anaemia.

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“…In the field of medical image processing, researchers have utilized various techniques to address diagnoses and predictions of eye-related diseases, including deep learning and hybrid algorithm for machine learning [ 10 – 15 ]. Similarly, recent advancements have introduced non-invasive techniques for Hb prediction, leveraging image processing and machine learning [ 16 – 18 ]. Notably, deep learning methodologies utilizing images of the fingertip and eye have been applied to categorize Hb levels [ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Real-time non-invasive hemoglobin prediction using deep learning-enabled smartphone imaging

Chen,

Hu,

Zhu

et al. 2024

BMC Med Inform Decis Mak

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Background Accurate measurement of hemoglobin concentration is essential for various medical scenarios, including preoperative evaluations and determining blood loss. Traditional invasive methods are inconvenient and not suitable for rapid, point-of-care testing. Moreover, current models, due to their complex parameters, are not well-suited for mobile medical settings, which limits the ability to conduct frequent and rapid testing. This study aims to introduce a novel, compact, and efficient system that leverages deep learning and smartphone technology to accurately estimate hemoglobin levels, thereby facilitating rapid and accessible medical assessments. Methods The study employed a smartphone application to capture images of the eye, which were subsequently analyzed by a deep neural network trained on data from invasive blood test data. Specifically, the EGE-Unet model was utilized for eyelid segmentation, while the DHA(C3AE) model was employed for hemoglobin level prediction. The performance of the EGE-Unet was evaluated using statistical metrics including mean intersection over union (MIOU), F1 Score, accuracy, specificity, and sensitivity. The DHA(C3AE) model’s performance was assessed using mean absolute error (MAE), mean-square error (MSE), root mean square error (RMSE), and R^2. Results The EGE-Unet model demonstrated robust performance in eyelid segmentation, achieving an MIOU of 0.78, an F1 Score of 0.87, an accuracy of 0.97, a specificity of 0.98, and a sensitivity of 0.86. The DHA(C3AE) model for hemoglobin level prediction yielded promising outcomes with an MAE of 1.34, an MSE of 2.85, an RMSE of 1.69, and an R^2 of 0.34. The overall size of the model is modest at 1.08 M, with a computational complexity of 0.12 FLOPs (G). Conclusions This system presents a groundbreaking approach that eliminates the need for supplementary devices, providing a cost-effective, swift, and accurate method for healthcare professionals to enhance treatment planning and improve patient care in perioperative environments. The proposed system has the potential to enable frequent and rapid testing of hemoglobin levels, which can be particularly beneficial in mobile medical settings. Trial Registration The clinical trial was registered on the Chinese Clinical Trial Registry (No. ChiCTR2100044138) on 20/02/2021.

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Detection of anaemia using medical images: A comparative study of machine learning algorithms – A systematic literature review

Cited by 11 publications

References 38 publications

Prediction of Anemia using various Ensemble Learning and Boosting Techniques

Prediction of Anemia using various Ensemble Learning and Boosting Techniques

Automated Anaemia Detection From Conjunctiva Images : A Machine Learning Approach for Android Application

Real-time non-invasive hemoglobin prediction using deep learning-enabled smartphone imaging

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