MNPDenseNet: Automated Monkeypox Detection Using Multiple Nested Patch Division and Pretrained DenseNet201

MNPDenseNet: Automated Monkeypox Detection Using Multiple Nested Patch Division and Pretrained DenseNet201

Background
Monkeypox is a viral disease caused by the monkeypox virus (MPV). A surge in monkeypox infection has been reported since early May 2022, and the outbreak has been classified as a global health emergency as the situation continues to worsen. Early and accurate detection of the disease is required to control its spread. Machine learning methods offer fast and accurate detection of COVID-19 from chest X-rays, and chest computed tomography (CT) images. Likewise, computer vision techniques can automatically detect monkeypoxes from digital images, videos, and other inputs.

Objectives
In this paper, we propose an automated monkeypox detection model as the first step toward controlling its global spread.

Materials and method
A new dataset comprising 910 open-source images classified into five categories (healthy, monkeypox, chickenpox, smallpox, and zoster zona) was created. A new deep feature engineering architecture was proposed, which contained the following components: (i) multiple nested patch division, (ii) deep feature extraction, (iii) multiple feature selection by deploying neighborhood component analysis (NCA), Chi2, and ReliefF selectors, (iv) classification using SVM with 10-fold cross-validation, (v) voted results generation by deploying iterative hard majority voting (IHMV) and (vi) selection of the best vector by a greedy algorithm.

Results
Our proposal attained a 91.87% classification accuracy on the collected dataset. This is the best result of our presented framework, which was automatically selected from 70 generated results.

Conclusions
The computed classification results and findings demonstrated that monkeypox could be successfully detected using our proposed automated model.