MonkeyNet: A robust deep convolutional neural network for monkeypox disease detection and classification

Bala, Diponkor; Hossain, Md. Shamim; Hossain, Mohammad Alamgir; Abdullah, Md. Ibrahim; Rahman, Md. Mizanur; Manavalan, Balachandran; Gu, Naijie; Islam, Mohammad S.; Huang, Zhangjin

doi:10.1016/j.neunet.2023.02.022

Cited by 61 publications

(32 citation statements)

References 39 publications

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“…The connection between each layer was present in each dense block, which made it more effective and deeper to train and extract more useful features for the network 30 . It allowed more efficient features to be reused and had over half of the parameters less than Resnet, which significantly reduced the number of parameters and could more effectively mitigate the overfitting and eliminated the gradient disappearance problem 30–34 . In the model, a pair of ROIs segmented from PET/CT was used as input and the probabilities of two categories (0 and 1) were used as output, respectively, in the model.…”

Section: Methodsmentioning

confidence: 99%

“…30 It allowed more efficient features to be reused and had over half of the parameters less than Resnet, which significantly reduced the number of parameters and could more effectively mitigate the overfitting and eliminated the gradient disappearance problem. [30][31][32][33][34] In the model, a pair of ROIs segmented from PET/CT was used as input and the probabilities of two categories (0 and 1) were used as output, respectively, in the model. Each input branch included 1 convolution module, 4 dense modules, and 4 transition modules.…”

Section: Deep Learning Features Extractionmentioning

confidence: 99%

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Prognostic Value of a Combined Nomogram Model Integrating 3-Dimensional Deep Learning and Radiomics for Head and Neck Cancer

Li,

Xie,

Liu

et al. 2024

J Comput Assist Tomogr

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Objective The preoperative prediction of the overall survival (OS) status of patients with head and neck cancer (HNC) is significant value for their individualized treatment and prognosis. This study aims to evaluate the impact of adding 3D deep learning features to radiomics models for predicting 5-year OS status. Methods Two hundred twenty cases from The Cancer Imaging Archive public dataset were included in this study; 2212 radiomics features and 304 deep features were extracted from each case. The features were selected by univariate analysis and the least absolute shrinkage and selection operator, and then grouped into a radiomics model containing Positron Emission Tomography /Computed Tomography (PET/CT) radiomics features score, a deep model containing deep features score, and a combined model containing PET/CT radiomics features score +3D deep features score. TumorStage model was also constructed using initial patient tumor node metastasis stage to compare the performance of the combined model. A nomogram was constructed to analyze the influence of deep features on the performance of the model. The 10-fold cross-validation of the average area under the receiver operating characteristic curve and calibration curve were used to evaluate performance, and Shapley Additive exPlanations (SHAP) was developed for interpretation. Results The TumorStage model, radiomics model, deep model, and the combined model achieved areas under the receiver operating characteristic curve of 0.604, 0.851, 0.840, and 0.895 on the train set and 0.571, 0.849, 0.832, and 0.900 on the test set. The combined model showed better performance of predicting the 5-year OS status of HNC patients than the radiomics model and deep model. The combined model was shown to provide a favorable fit in calibration curves and be clinically useful in decision curve analysis. SHAP summary plot and SHAP The SHAP summary plot and SHAP force plot visually interpreted the influence of deep features and radiomics features on the model results. Conclusions In predicting 5-year OS status in patients with HNC, 3D deep features could provide richer features for combined model, which showed outperformance compared with the radiomics model and deep model.

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Section: Methodsmentioning

confidence: 99%

Section: Deep Learning Features Extractionmentioning

confidence: 99%

Prognostic Value of a Combined Nomogram Model Integrating 3-Dimensional Deep Learning and Radiomics for Head and Neck Cancer

Li,

Xie,

Liu

et al. 2024

J Comput Assist Tomogr

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“…This is so due to the fact that optimal weights for deep models might be adjusted. The "Monkeypox Skin Images Dataset," or "MSID", 39 was created specifically for the sake of this investigation. The weighted accuracy and the Grad-CAM are both shown in this version.…”

Section: Other Skin Diseasesmentioning

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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“…They considered three pretrained base learners Xception, Inception v3, and DenseNet169 to fine-tune the target data set, with an average 93.39% accuracy, 88.91% precision, 96.78% recall, and 92.35% F1 score as returned by their proposed model. Bala et al evaluated a modified DenseNet-201 deep learning-based CNN model with mpox images data set . The model correctly identified mpox with 93.19 and 98.91% accuracies, using original and augmented data sets, respectively.…”

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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MonkeyNet: A robust deep convolutional neural network for monkeypox disease detection and classification

Cited by 61 publications

References 39 publications

Prognostic Value of a Combined Nomogram Model Integrating 3-Dimensional Deep Learning and Radiomics for Head and Neck Cancer

Prognostic Value of a Combined Nomogram Model Integrating 3-Dimensional Deep Learning and Radiomics for Head and Neck Cancer

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

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

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