Monkeypox Diagnostic-Aid System with Skin Images Using Convolutional Neural Networks

Muñoz-Saavedra, Luis; Escobar-Linero, Elena; Civit-Masot, Javier; Luna-Perejón, Francisco; Balcells, A; Domínguez-Morales, Manuel

doi:10.2139/ssrn.4186534

Cited by 9 publications

(10 citation statements)

References 15 publications

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“…Moreover, the attention architecture implemented in this research work performs better than ensemble technique in [30] by the increase in accuracy of about 11%. Also, the proposed technique (SENet+InceptionV3) succeeds to match the accuracy of 98% achieved by ensemble technique in the paper [33]. Hence, overall it can be observed that attention model do have significant results alike ensemble technique.…”

supporting

confidence: 59%

“…Next, a balanced dataset of Monkeypox, Healthy and Other Diseases-"Monkeypox Skin Dataset" based on three parameters viz. classes, types and sizes of images is created in [33]. The proposed work and created dataset are compared with the previous two datasets-"Monkeypox 2022" ( [27]) and "Monkeypox Skin Lesion Dataset (MSLD)" ( [28]) in terms of class labels and balanced number of images per class.…”

Section: Related Workmentioning

confidence: 99%

“…On the other side, the attention mechanisms are also getting trained to allow the focus on definite portion of the input [31]. The attempts are also made to create authenticated image datasets from the existing resources (websites, newspapers, other articles) [28,32,33]. However, the existing literature suffers from the research gaps as follows:…”

Section: Introductionmentioning

confidence: 99%

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An Enhanced Diagnosis of Monkeypox Disease Using Deep Learning and a Novel Attention Model Senet on Diversified Dataset

Surati

Trivedi²,

Shrimali

et al. 2023

MTI

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With the widespread of Monkeypox and increase in the weekly reported number of cases, it is observed that this outbreak continues to put the human beings in risk. The early detection and reporting of this disease will help monitoring and controlling the spread of it and hence, supporting international coordination for the same. For this purpose, the aim of this paper is to classify three diseases viz. Monkeypox, Chikenpox and Measles based on provided image dataset using trained standalone DL models (InceptionV3, EfficientNet, VGG16) and Squeeze and Excitation Network (SENet) Attention model. The first step to implement this approach is to search, collect and aggregate (if require) verified existing dataset(s). To the best of our knowledge, this is the first paper which has proposed the use of SENet based attention models in the classification task of Monkeypox and also targets to aggregate two different datasets from distinct sources in order to improve the performance parameters. The unexplored SENet attention architecture is incorporated with the trunk branch of InceptionV3 (SENet+InceptionV3), EfficientNet (SENet+EfficientNet) and VGG16 (SENet+VGG16) and these architectures improve the accuracy of the Monkeypox classification task significantly. Comprehensive experiments on three datasets depict that the proposed work achieves considerably high results with regard to accuracy, precision, recall and F1-score and hence, improving the overall performance of classification. Thus, the proposed research work is advantageous in enhanced diagnosis and classification of Monkeypox that can be utilized further by healthcare experts and researchers to confront its outspread.

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supporting

confidence: 59%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Enhanced Diagnosis of Monkeypox Disease Using Deep Learning and a Novel Attention Model Senet on Diversified Dataset

Surati

Trivedi²,

Shrimali

et al. 2023

MTI

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“…Luis Muñoz-Saavedra et al [14] developed MPX diagnostic-aid system using convolutional neural networks (CNN) and a skin image dataset. The authors compared the performance of using the CNN model and CNN integrated with ResNet50, EfficientNet-B0, and MobileNet-V2.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep learning Model for Recognizing Monkey Pox based on Dense net-121 Algorithm

Torky

Bakheit²,

Bakry³

et al. 2022

Preprint

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While the world is trying to get rid of the Covid 19 pandemic, the beginning of the monkeypox(MPX) pandemic has recently appeared and is threatening many countries of the world. MPX is a rare disease caused by infection with the MPX virus, and it is among the same family of pox viruses. The danger is that MPX causes pustules all over the body, which causes a revolting view to the body regions and works as a source of infection in case of skin contact between individuals. Pustules and rashes are common symptoms of many pox viruses and other skin diseases such as Measles, chicken pox, syphilis, Eczema, etc, Therefore, the medical and clinical diagnosis of monkeypox is one of the great challenges for doctors and specialists. In response to this need, Artificial intelligence can develop aid systems based on machine and deep learning algorithms for diagnosing these types of diseases based on datasets of skin images to those types of diseases. In this paper, a deep learning approach called Dense Net-121model is applied, tested, and compared with the convolution neural network (CNN) model for diagnosing monkeypox through a skin image dataset of MPX and Measles images. The most significant finding to emerge from this study is the superiority of the Dense Net-121 model over CNN in diagnosing MPX cases with a testing accuracy of 93%. These findings suggest a role for using more deep learning algorithms for accurately diagnosing MPX cases with bigger datasets of similar pustules and rashes diseases.

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“…The deep learning convolutional neural network (CNN) technique has shown good performance in image-based research, including in the diagnosis of monkeypox. Several studies have used architectures such as vgg-19 and ResNet50 with accuracy reaching 93.33% and 82.96% respectively [8], [9]. However, ResNet50 has disadvantages in terms of computational resource efficiency due to the large number of parameters and layers, and is prone to overfitting on small datasets [10].…”

Section: Introductionmentioning

confidence: 99%

Monkeypox Classification based on Skin Images using CNN: EfficientNet-B0

Eko Niti Taruno,

Satya Nugraha,

Dwiyansaputra

et al. 2023

E3S Web of Conf.

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Monkeypox is a zoonotic infectious disease caused by a virus of the orthopoxvirus genus. It can infect humans, vertebrates, and arthropods. Transmission to humans occurs through direct contact with infected animal body fluids or consumption of undercooked meat. Monkeypox cases have been reported globally, with thousands of confirmed cases and several deaths. Early symptoms include fever, rash, swollen lymph nodes, back pain, and headache. Diagnosis can be made through physical examination and laboratory tests. Imagebased artificial intelligence technology, specifically the EfficientNet-B0 architecture, has been proposed as a solution for the classification of monkeypox based on skin lesion images. The research aims to compare the performance of EfficientNetB0 with other CNN architectures and contribute to the development of medical image classification technology. Among the models evaluated, the EfficientNet-B0 model emerged as the standout performer, achieving an accuracy of 85.12%, surpassing the accuracy of other models such as MobileNet (63.63%) and InceptionV3 (71.4%). EfficientNet-B0 also demonstrated strong sensitivity (78.46%) and impressive specificity (91.78%), outperforming other models in these metrics. Additionally, despite not surpassing the accuracy of ResNet-50 (87.59%), EfficientNet-B0 achieved its accuracy with approximately four times fewer parameters, highlighting its efficiency in parameter usage and computational resources. These results can help improve models and aid in clinical decision-making.

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Monkeypox Diagnostic-Aid System with Skin Images Using Convolutional Neural Networks

Cited by 9 publications

References 15 publications

An Enhanced Diagnosis of Monkeypox Disease Using Deep Learning and a Novel Attention Model Senet on Diversified Dataset

An Enhanced Diagnosis of Monkeypox Disease Using Deep Learning and a Novel Attention Model Senet on Diversified Dataset

Deep learning Model for Recognizing Monkey Pox based on Dense net-121 Algorithm

Monkeypox Classification based on Skin Images using CNN: EfficientNet-B0

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