Can Artificial Intelligence Detect Monkeypox from Digital Skin Images?

Islam, Towhidul; Hussain, Munira; Chowdhury, Forhad Uddin Hasan; Islam, Bashima

doi:10.1101/2022.08.08.503193

Cited by 31 publications

(23 citation statements)

References 27 publications

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“…Several studies have investigated the reliability of artificial intelligence in diagnosing Monkeypox using digital skin images. The study by [14] decries the rarity of Monkeypox as the cause of the knowledge gap, which inspired the investigation. The source employed deep machine learning techniques in sourcing, preparing, and testing the image data.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Pictorial data on the disease is still scanty. Many studies, such as [14,15], have constructed databases of Monkeypox images that the researcher could have used. Nevertheless, the researcher was interested in conducting primary research, and contributing to the discourse by giving an independent opinion about the viability of machine learning models in detecting the disease.…”

Section: A Data Collectionmentioning

confidence: 99%

“…Furthermore, the data was augmented by adjusting brightness, rotating, modifying sharpness, zooming, and shearing. In the end, the five augmentation techniques produced 1000 images out of the 200 original images [14]. Fig.…”

Section: A Data Collectionmentioning

confidence: 99%

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Deep Learning Models for the Detection of Monkeypox Skin Lesion on Digital Skin Images

Alrusaini¹

2023

IJACSA

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The study is an investigation testing the accuracy of deep learning models in the detection of Monkeypox. The disease is relatively new and difficult for physicians to detect. Data for the skins were obtained from Google via web-scraping with Python's BeautifulSoup, SERP API, and requests libraries. The images underwent scrutiny by professional physicians to determine their validity and classification. The researcher extracted the images' features using two CNN models -GoogLeNet and ResNet50. Feature selection from the images involved conducting principal component analysis. Classification employed Support Vector Machines, ResNet50, VGG-16, SqueezeNet, and InceptionV3 models. The results showed that all the models performed relatively the same. However, the most effective model was VGG-16 (accuracy = 0.96, F1-score = 0.92). It is an affirmation of the usefulness of artificial intelligence in the detection of the Monkeypox disease. Subject to the approval of national health authorities, the technology can be used to help detect the disease faster and more conveniently. If integrated into a mobile application, it can be members of the public to selfdiagnose before seeking official diagnoses from approved hospitals. The researcher recommends further research into the models and building bigger image databases that will power more reliable analyses.

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Section: Literature Reviewmentioning

confidence: 99%

Section: A Data Collectionmentioning

confidence: 99%

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Deep Learning Models for the Detection of Monkeypox Skin Lesion on Digital Skin Images

Alrusaini¹

2023

IJACSA

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“…For example, Islam et al. (2022) used ShuffleNet-V2 to attain a maximum F-measure of 0.67 and a precision of 0.79 ( Islam, Hussain, Chowdhury, & Islam, 2022 ). Using ResNet50, Ali et al.…”

Section: Discussionmentioning

confidence: 99%

Deep transfer learning approaches for Monkeypox disease diagnosis

Ahsan

Uddin

Ali

et al. 2023

Expert Systems with Applications

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“…The study had precision of 85%. To train deep models, however, one needs training samples that are significantly bigger if one wants to achieve more reliable detection power [ 55 ].…”

Section: Literature Reviewmentioning

confidence: 99%

An Al-Biruni Earth Radius Optimization-Based Deep Convolutional Neural Network for Classifying Monkeypox Disease

et al. 2022

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Human skin diseases have become increasingly prevalent in recent decades, with millions of individuals in developed countries experiencing monkeypox. Such conditions often carry less obvious but no less devastating risks, including increased vulnerability to monkeypox, cancer, and low self-esteem. Due to the low visual resolution of monkeypox disease images, medical specialists with high-level tools are typically required for a proper diagnosis. The manual diagnosis of monkeypox disease is subjective, time-consuming, and labor-intensive. Therefore, it is necessary to create a computer-aided approach for the automated diagnosis of monkeypox disease. Most research articles on monkeypox disease relied on convolutional neural networks (CNNs) and using classical loss functions, allowing them to pick up discriminative elements in monkeypox images. To enhance this, a novel framework using Al-Biruni Earth radius (BER) optimization-based stochastic fractal search (BERSFS) is proposed to fine-tune the deep CNN layers for classifying monkeypox disease from images. As a first step in the proposed approach, we use deep CNN-based models to learn the embedding of input images in Euclidean space. In the second step, we use an optimized classification model based on the triplet loss function to calculate the distance between pairs of images in Euclidean space and learn features that may be used to distinguish between different cases, including monkeypox cases. The proposed approach uses images of human skin diseases obtained from an African hospital. The experimental results of the study demonstrate the proposed framework’s efficacy, as it outperforms numerous examples of prior research on skin disease problems. On the other hand, statistical experiments with Wilcoxon and analysis of variance (ANOVA) tests are conducted to evaluate the proposed approach in terms of effectiveness and stability. The recorded results confirm the superiority of the proposed method when compared with other optimization algorithms and machine learning models.

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Can Artificial Intelligence Detect Monkeypox from Digital Skin Images?

Cited by 31 publications

References 27 publications

Deep Learning Models for the Detection of Monkeypox Skin Lesion on Digital Skin Images

Deep Learning Models for the Detection of Monkeypox Skin Lesion on Digital Skin Images

Deep transfer learning approaches for Monkeypox disease diagnosis

An Al-Biruni Earth Radius Optimization-Based Deep Convolutional Neural Network for Classifying Monkeypox Disease

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