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

Mazzu-Nascimento, Thiago; Evangelista, Danilo Nogueira; Abubakar, Obeedu; Sousa, Amanda Soares; Souza, Leandro Cândido de; Chachá, Silvana Gama Florêncio; Luporini, Rafael Luís; Domingues, Lucas Vinicius; Silva, Diego Furtado; Nogueira‐de‐Almeida, Carlos Alberto

doi:10.1055/s-0041-1734014

Cited by 6 publications

(6 citation statements)

References 22 publications

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“…Regards the invasive method of detecting anemia, which is costive, time-consuming, and painful to patients due to the extraction of blood and sometimes exposes clinicians to prick the cause of the blood extraction. The non-invasive approach is cost-effective, takes less time and is reliable through the use of the palm, conjunctiva, tongue and fingernails as compared to the invasive method, even though these human features can be used to detect anemia by assessing their paleness by medical officers, this is mostly left to the discretion of the physician or the health official [ 11 ]. In this study, we aim at using the pallor of the palm to detect anemia using machine learning algorithms through a comparative study of Decision Tree, SVM, Naïve Bayes, k-NN and CNN, since the palm is one of the essentials sites or features to detect anemia [ 12 ], particularly at the initial stage of the examination.…”

Section: Introductionmentioning

confidence: 99%

Detection of iron deficiency anemia by medical images: a comparative study of machine learning algorithms

et al. 2023

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Background Anemia is one of the global public health problems that affect children and pregnant women. Anemia occurs when the level of red blood cells within the body decreases or when the structure of the red blood cells is destroyed or when the Hb level in the red blood cell is below the normal threshold, which results from one or more increased red cell destructions, blood loss, defective cell production or a depleted sum of Red Blood Cells. Methods The method used in this study is divided into three phases: the datasets were gathered, which is the palm, pre-processed the image, which comprised; Extracted images, and augmented images, segmented the Region of Interest of the images and acquired their various components of the CIE L*a*b* colour space (also referred to as the CIELAB), and finally developed the proposed models for the detection of anemia using the various algorithms, which include CNN, k-NN, Nave Bayes, SVM, and Decision Tree. The experiment utilized 527 initial datasets, rotation, flipping and translation were utilized and augmented the dataset to 2635. We randomly divided the augmented dataset into 70%, 10%, and 20% and trained, validated and tested the models respectively. Results The results of the study justify that the models performed appropriately when the palm is used to detect anemia, with the Naïve Bayes achieving a 99.96% accuracy while the SVM achieved the lowest accuracy of 96.34%, as the CNN also performed better with an accuracy of 99.92% in detecting anemia. Conclusions The invasive method of detecting anemia is expensive and time-consuming; however, anemia can be detected through the use of non-invasive methods such as machine learning algorithms which is efficient, cost-effective and takes less time. In this work, we compared machine learning models such as CNN, k-NN, Decision Tree, Naïve Bayes, and SVM to detect anemia using images of the palm. Finally, the study supports other similar studies on the potency of the Machine Learning Algorithm as a non-invasive method in detecting iron deficiency anemia.

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Section: Introductionmentioning

confidence: 99%

Detection of iron deficiency anemia by medical images: a comparative study of machine learning algorithms

et al. 2023

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“…where f (s) = s 1 3 , for s > 0.008856 And f (s) = 7.787s + 16 116 , for s ≤ 0.008856 The color difference ΔE between two colors in the CIE L*a*b* (CIELAB) color space is;…”

Section: Anemia Detection Conditionmentioning

confidence: 99%

“…9,14 In several cases, the clinical sign for the detection of anemia will be quite helpful, but the lack of agreement between observers on several things and the low sensitivity of the conjunctiva color will undermine the authenticity of the visual detection method. 15,16 This examination is not precise and is not reliable since the quality of these findings depends on the decision and training of the health officer. 17 Numerous emerging geographical areas are vulnerable to anemia due to inadequate health and medical facilities with few health workers, such as medical officers and laboratory technicians or biomedical scientists.…”

Section: Introductionmentioning

confidence: 99%

“…In several cases, the clinical sign for the detection of anemia will be quite helpful, but the lack of agreement between observers on several things and the low sensitivity of the conjunctiva color will undermine the authenticity of the visual detection method 15,16 . This examination is not precise and is not reliable since the quality of these findings depends on the decision and training of the health officer 17 …”

Section: Introductionmentioning

confidence: 99%

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Iron deficiency anemia detection using machine learning models: A comparative study of fingernails, palm and conjunctiva of the eye images

Asare

Appiahene

Donkoh

et al. 2023

Engineering Reports

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Anemia is one of the global public health challenges that particularly affect children and pregnant women. A study by WHO indicates that 42% of children below the age of 6 and 40% of pregnant women worldwide are anemic. This affects the world's total population by 33%, due to the cause of iron deficiency. The non‐invasive technique, such as the use of machine learning algorithms is one of the methods used in the diagnosis or detection of clinical diseases, which anemia detection cannot be overlooked in recent days. In this study, a machine learning approach was used to detect iron‐deficiency anemia with the application of Naïve Bayes, CNN, SVM, k‐NN, and decision tree algorithms. This enabled us to compare the conjunctiva of the eyes, the palpable palm, and the color of the fingernail images to justify which of them has a higher accuracy for detecting anemia in children. The method utilized was categorized into three different stages: dataset collection, dataset preprocessing, and model development for anemia detection. The CNN achieved a higher accuracy of 99.12%, while the SVM had the least accuracy of 95.4%. The performance of the models justifies that the non‐invasive approach is an effective mechanism for anemia detection.

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“…In several cases, the clinical sign for the detection of anemia will move and be quite helpful, but the lack of agreement between observers on several things and the low sensitivity of the conjunctiva colour will undermine the authenticity of the visual detection method [13], [14]. This examination is not precise and is not reliable since the quality of these findings depends on the decision and training of the health officer [15].…”

Section: Introductionmentioning

confidence: 99%

Iron Deficiency Anemia Detection using Machine Learning Models: A Comparative Study of Fingernails, Palm and Conjunctiva of the Eye Images.

Asare

Appiahene

Donkoh

et al. 2023

Preprint

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Anemia is one of the global public health challenges that particularly affect children and pregnant women. A study by WHO indicates that 42% of children below 6 years and 40% of pregnant women worldwide are anemic. This affects the world’s total population by 33%, due to the cause of iron deficiency. The non-invasive technique, such as the use of machine learning algorithms, is one of the methods used in the diagnosing or detection of clinical diseases, which anemia detection cannot be overlooked in recent days. In this study, machine learning algorithms were used to detect iron-deficiency anemia with the application of Naïve Bayes, CNN, SVM, k-NN, and Decision Tree. This enabled us to compare the conjunctiva of the eyes, the palpable palm, and the colour of the fingernail images to justify which of them has a higher accuracy for detecting anemia in children. The technique utilized in this study was categorized into three different stages: collecting of datasets (conjunctiva of the eyes, fingernails and the palpable palm images), preprocessing the images; image extraction, segmentation of the Region of Interest of the images, obtained each component of the CIE L*a*b* colour space (CIELAB). The models were then developed for the detection of anemia using various algorithms. The CNN had an accuracy of 99.12% in the detection of anemia, followed by the Naïve Bayes with an accuracy of 98.96%, while Decision Tree and k-NN had 98.29% and 98.92% accuracy respectively. However, the SVM had the least accuracy of 95.4% on the palpable palm. The performance of the models justifies that the non-invasive approach is an effective mechanism for anemia detection. Keywords: Iron deficiency, anemia, non-invasive, machine learning, data augmentation, algorithms, region of interest.

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

Cited by 6 publications

References 22 publications

Detection of iron deficiency anemia by medical images: a comparative study of machine learning algorithms

Detection of iron deficiency anemia by medical images: a comparative study of machine learning algorithms

Iron deficiency anemia detection using machine learning models: A comparative study of fingernails, palm and conjunctiva of the eye images

Iron Deficiency Anemia Detection using Machine Learning Models: A Comparative Study of Fingernails, Palm and Conjunctiva of the Eye Images.

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