Deep transfer learning approaches for Monkeypox disease diagnosis

Ahsan, Md Manjurul; Uddin, Muhammad Ramiz; Ali, Md Shahin; Islam, Md. Khairul; Farjana, Mithila; Sakib, Ahmed Nazmus; Momin, Khondhaker Al; Luna, Shahana Akter

doi:10.1016/j.eswa.2022.119483

Cited by 87 publications

(55 citation statements)

References 63 publications

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“…21 Ali et al detected and classified mpox skin lesion from a data set that contained the images of measles, chickenpox, and mpox skin lesions. 22 27 and tested the model on ten different CNN models. Combined with Xception, the proposed model distinguished with and without mpox cases with 77−88% accuracy.…”

Section: Review Of the Related Workmentioning

confidence: 99%

Deep and Transfer Learning Approaches for Automated Early Detection of Monkeypox (Mpox) Alongside Other Similar Skin Lesions and Their Classification

Pal,

Mahal,

Mohapatra

et al. 2023

ACS Omega

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The world faces multiple public health emergencies simultaneously, such as . mpox, from being a neglected disease, has emerged as a global threat that has spread to more than 100 nonendemic countries, even as COVID-19 has been spreading for more than 3 years now. The general mpox symptoms are similar to chickenpox and measles, thus leading to a possible misdiagnosis. This study aimed at facilitating a rapid and high-brevity mpox diagnosis. Reportedly, mpox circulates among particular groups, such as sexually promiscuous gay and bisexuals. Hence, selectively vaccinating, isolating, and treating them seems difficult due to the associated social stigma. Deep learning (DL) has great promise in image-based diagnosis and could help in error-free bulk diagnosis. The novelty proposed, the system adopted, and the methods and approaches are discussed in the article. The present work proposes the use of DL models for automated early mpox diagnosis. The performances of the proposed algorithms were evaluated using the data set available in public domain. The data set adopted for the study was meant for both training and testing, the details of which are elaborated. The performances of CNN, VGG19, ResNet 50, Inception v3, and Autoencoder algorithms were compared. It was concluded that CNN, VGG19, and Inception v3 could help in early detection of mpox skin lesions, and Inception v3 returned the best (96.56%) classification accuracy.

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Section: Review Of the Related Workmentioning

confidence: 99%

Deep and Transfer Learning Approaches for Automated Early Detection of Monkeypox (Mpox) Alongside Other Similar Skin Lesions and Their Classification

Pal,

Mahal,

Mohapatra

et al. 2023

ACS Omega

View full text Add to dashboard Cite

show abstract

“…Due to the monkeypox pandemic, monkeypox detection studies have recently been popular in the literature. In the study of Ahsan et al, [ 33 ] monkeypox‐infected skin images were collected from Google and analyzed using deep learning approaches. Ali et al [ 22 ] created a human monkeypox image dataset.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning‐Based Skin Diseases Classification using Smartphones

Oztel,

Yolcu Oztel,

Sahin

2023

Advanced Intelligent Systems

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Skin disease recognition is one of the essential topics in the medical industry. Detecting skin disease from appearance can be difficult due to the similar appearance of skin lesions. In some cases, such as the monkeypox virus, the illness must be quickly determined, and the patients must be isolated to reduce the spreading of the disease. This study aims to create a deep learning‐based automated intelligent mobile application to detect skin disease. First, different small‐size pretrained networks are trained for skin lesion image classification. Then, the most suitable network from the viewpoint of both performance and mobile compatibility is transformed into the TensorFlow Lite format. Finally, a mobile application is created on the Android platform that utilizes the smartphone's camera to obtain images and uses TensorFlow Lite to make predictions. The proposed system produces 74.27% classification accuracy for seven classes on a combined dataset. It produces comparable/better results compared to the literature. Owing to the proposed system, the patients can make a preliminary diagnosis of their lesions using their smartphones. Thus, risky patients can be encouraged to visit the hospital for a definitive diagnosis. In addition, the mobile application can avoid undue stress and false alarms.

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“…This approach obtains detection accuracy of

79.26 %

. Ahsan et al collected image dataset of monkeypox, chickenpox, measles, and normal cases and evaluated this dataset using modified VGG16 network 28 . This approach achieves detection accuracy of

83 %

without data augmentation, whereas the detection accuracy is

78 %

with data augmentation.…”

Section: Related Workmentioning

confidence: 99%

An effective human monkeypox classification using vision transformer

Aloraini

2023

Int J Imaging Syst Tech

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Monkeypox virus has become a popular disease as many people have been recently infected with this virus around the world. This virus causes fears of another global pandemic similar to COVID‐19 pandemic. Therefore, it is important to determine infected people at an early stage to reduce the spread of this virus. Creating an automatic approach to detect monkeypox virus is a challenging problem due to the similar appearance between skin infections. Currently, deep convolutional neural network approaches have been used in detecting and classifying skin infections. However, these approaches sever from certain challenges such as being unable to capture the global context and low accuracy rate. In this paper, we propose a novel approach based on vision transformer to automatically detect monkeypox virus in skin images. We fine‐tune vision transformer for the human monkeypox classification problems. This fine‐tuned vision transformer splits an image into image patches and then these patches are used as input to the transformer in a sequence structure, followed by added layers to detect monkeypox virus. The experimental results show that the proposed approach achieves excellent detection accuracy using a public dataset.

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Deep transfer learning approaches for Monkeypox disease diagnosis

Cited by 87 publications

References 63 publications

Deep and Transfer Learning Approaches for Automated Early Detection of Monkeypox (Mpox) Alongside Other Similar Skin Lesions and Their Classification

Deep and Transfer Learning Approaches for Automated Early Detection of Monkeypox (Mpox) Alongside Other Similar Skin Lesions and Their Classification

Deep Learning‐Based Skin Diseases Classification using Smartphones

An effective human monkeypox classification using vision transformer

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