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

Alrusaini, Othman

doi:10.14569/ijacsa.2023.0140170

Cited by 18 publications

(14 citation statements)

References 23 publications

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“…The CNN model attained outstanding test accuracy of 99.6% and a weighted average precision, recall, and F1 score of 0.99. Likewise, similar studies were conducted by authors in [ 16 – 20 ]. A comparative summary of related works is presented in Table 1 .…”

Section: Related Worksupporting

confidence: 68%

Blockchain-enabled healthcare monitoring system for early Monkeypox detection

2023

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The recent emergence of monkeypox poses a life-threatening challenge to humans and has become one of the global health concerns after COVID-19. Currently, machine learning-based smart healthcare monitoring systems have demonstrated significant potential in image-based diagnosis including brain tumor identification and lung cancer diagnosis. In a similar fashion, the applications of machine learning can be utilized for the early identification of monkeypox cases. However, sharing critical health information with various actors such as patients, doctors, and other healthcare professionals in a secure manner remains a research challenge. Motivated by this fact, our paper presents a blockchain-enabled conceptual framework for the early detection and classification of monkeypox using transfer learning. The proposed framework is experimentally demonstrated in Python 3.9 using a monkeypox dataset of 1905 images obtained from the GitHub repository. To validate the effectiveness of the proposed model, various performance estimators, namely accuracy, recall, precision, and F1-score, are employed. The performance of different transfer learning models, namely Xception, VGG19, and VGG16, is compared against the presented methodology. Based on the comparison, it is evident that the proposed methodology effectively detects and classifies the monkeypox disease with a classification accuracy of 98.80%. In future, multiple skin diseases such as measles and chickenpox can be diagnosed using the proposed model on the skin lesion datasets.

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Section: Related Worksupporting

confidence: 68%

Blockchain-enabled healthcare monitoring system for early Monkeypox detection

2023

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“…The other dataset utilized to conduct the experiments of this article is called Monkeypox Skin Lesion Dataset. 43 The dataset contains 228 photographs, 102 of which are from the monkeypox category and 126 from the non-monkeypox category. Instances of photos available in each category are displayed in Figure 2 .…”

Section: Methodsmentioning

confidence: 99%

“…Furthermore, the study 42 utilized six CNNs separately to distinguish images with monkeypox from images with other diseases and normal cases using another new dataset collected from online sources called Monkeypox Skin Lesion Dataset (MSLD). 43 These CNNs include ResNet-18, MobileNet, NasNetMobile, GoogLeNet, EfficientB0, and ShuffleNet, where the highest performance was achieved with MobileNet. The same dataset (MSLD) was used in 44 to train the ResNet-18 CNN model with TL.…”

Section: Literature Reviewmentioning

confidence: 99%

MonDiaL-CAD: Monkeypox diagnosis via selected hybrid CNNs unified with feature selection and ensemble learning

Attallah

2023

DIGITAL HEALTH

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Objective Recently, monkeypox virus is slowly evolving and there are fears it will spread as COVID-19. Computer-aided diagnosis (CAD) based on deep learning approaches especially convolutional neural network (CNN) can assist in the rapid determination of reported incidents. The current CADs were mostly based on an individual CNN. Few CADs employed multiple CNNs but did not investigate which combination of CNNs has a greater impact on the performance. Furthermore, they relied on only spatial information of deep features to train their models. This study aims to construct a CAD tool named “Monkey-CAD” that can address the previous limitations and automatically diagnose monkeypox rapidly and accurately. Methods Monkey-CAD extracts features from eight CNNs and then examines the best possible combination of deep features that influence classification. It employs discrete wavelet transform (DWT) to merge features which diminishes fused features' size and provides a time-frequency demonstration. These deep features’ sizes are then further reduced via an entropy-based feature selection approach. These reduced fused features are finally used to deliver a better representation of the input features and feed three ensemble classifiers. Results Two freely accessible datasets called Monkeypox skin image (MSID) and Monkeypox skin lesion (MSLD) are employed in this study. Monkey-CAD could discriminate among cases with and without Monkeypox achieving an accuracy of 97.1% for MSID and 98.7% for MSLD datasets respectively. Conclusions Such promising results demonstrate that the Monkey-CAD can be employed to assist health practitioners. They also verify that fusing deep features from selected CNNs can boost performance.

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“…Other persons, however, were able to achieve higher precision using the same images. In this study, we aim to evaluate deep learning models for their ability to detect Monkeypox 36 . The characteristics of the images were extracted using two different CNN models, namely, GoogLeNet and ResNet50.…”

Section: Literature Reviewmentioning

confidence: 99%

Leveraging deep neural networks to uncover unprecedented levels of precision in the diagnosis of hair and scalp disorders

Chowdhury,

Sultan,

Jahan

et al. 2024

Skin Research and Technology

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BackgroundHair and scalp disorders present a significant challenge in dermatology due to their clinical diversity and overlapping symptoms, often leading to misdiagnoses. Traditional diagnostic methods rely heavily on clinical expertise and are limited by subjectivity and accessibility, necessitating more advanced and accessible diagnostic tools. Artificial intelligence (AI) and deep learning offer a promising solution for more accurate and efficient diagnosis.MethodsThe research employs a modified Xception model incorporating ReLU activation, dense layers, global average pooling, regularization and dropout layers. This deep learning approach is evaluated against existing models like VGG19, Inception, ResNet, and DenseNet for its efficacy in accurately diagnosing various hair and scalp disorders.ResultsThe model achieved a 92% accuracy rate, significantly outperforming the comparative models, with accuracies ranging from 50% to 80%. Explainable AI techniques like Gradient‐weighted Class Activation Mapping (Grad‐CAM) and Saliency Map provided deeper insights into the model's decision‐making process.ConclusionThis study emphasizes the potential of AI in dermatology, particularly in accurately diagnosing hair and scalp disorders. The superior accuracy and interpretability of the model represents a significant advancement in dermatological diagnostics, promising more reliable and accessible diagnostic methods.

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

Cited by 18 publications

References 23 publications

Blockchain-enabled healthcare monitoring system for early Monkeypox detection

Blockchain-enabled healthcare monitoring system for early Monkeypox detection

MonDiaL-CAD: Monkeypox diagnosis via selected hybrid CNNs unified with feature selection and ensemble learning

Leveraging deep neural networks to uncover unprecedented levels of precision in the diagnosis of hair and scalp disorders

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