Multi-Model Deep Learning System for Screening Human Monkeypox Using Skin Images

Jain, Deepak Kumar; Gupta, Kapil; Bajaj, Varun; Hussain, Amir

doi:10.22541/au.171015937.76129301/v1

2024

DOI: 10.22541/au.171015937.76129301/v1

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Multi-Model Deep Learning System for Screening Human Monkeypox Using Skin Images

Deepak Kumar Jain,

Kapil Gupta,

Varun Bajaj

et al.

Abstract: sion dataset. Results: By amalgamating features extracted from all three CNNs and utilizing the medium Gaussian kernel of the SVM classifier, our proposed system achieves an outstanding av- erage classification accuracy of 90.4%. Conclusions: Our developed MPXCN-Net is suitable for test- ing with a large diversified dataset before being used in clin- ical settings.

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Cited by 1 publication

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Deep Learning Framework for Optimizing Early Detection of Measles Using Transfer Learning

Nouman Saleem,

Anam Ishaq,

Malaika Riaz

et al. 2024

IJBR

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Measles is a highly infectious viral disease that can have serious health consequences. Accurate and early diagnosis is crucial. This study aims to enhance automated classification and early detection of this disease. To address the class imbalance, we augmented the dataset of normal images. Spatial features were extracted using convolutional neural networks, and traditional classifiers, including support vector machine, Random Forest, logistic regression, and k-nearest neighbors were applied to these features. Initial classification accuracy based solely on spatial features was as follows: Random Forest 63%, SVM 63%, KNN 60%, and Logistic Regression 63%. Through 10-fold cross-validation, mean accuracies were recorded as 65% for RF, 62% for SVM, 60% for KNN, and 61% for LR. Despite these initial results, the implementation of transfer learning led to significant improvements. By extracting probabilistic features from spatial features using RF and KNN models and concatenating these derived features, classification accuracy was substantially enhanced. The improved model achieved 99% accuracy for RF, SVM, and LR, with KNN reaching 98%. Cross-validation confirmed the robustness of the models, with a mean accuracy of approximately 98% and minimal standard deviations of 0.01. The findings demonstrate that combining transfer learning with traditional classifiers improves the efficiency and accuracy of lesion images. This approach shows significant potential for clinical applications.

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Deep Learning Framework for Optimizing Early Detection of Measles Using Transfer Learning

Nouman Saleem,

Anam Ishaq,

Malaika Riaz

et al. 2024

IJBR

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show abstract

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Multi-Model Deep Learning System for Screening Human Monkeypox Using Skin Images

Cited by 1 publication

References 32 publications

Deep Learning Framework for Optimizing Early Detection of Measles Using Transfer Learning

Deep Learning Framework for Optimizing Early Detection of Measles Using Transfer Learning

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