Classification of deep features obtained from mpox images using Xception by selecting them with PCA and mRMR and using SVM

Year 2025, Volume: 9 Issue: 2, 385 - 401, 31.12.2025

Mehmet Zahit Uzun , Tarık Talan

https://doi.org/10.26650/acin.1617557

https://izlik.org/JA93RA47YR

Abstract

Mpox is a dsDNA virus that shows lesions on the skin similar to those of chickenpox, measles, and smallpox. Clinical examination is based on traditional methods such as Polymerase Chain Reaction (PCR) test kits and skin lesion testing by electron microscopy. The costly and time-consuming nature of PCR tests and the similarity of Mpox to chickenpox, measles, and smallpox make electron microscopy tests difficult for early diagnosis. In this study, we attempted to solve these challenges by using transfer learning-based convolutional neural network (CNN) models to diagnose Mpox disease. The experiments were conducted on the publicly available Mpox Close Skin Images (MCSI) dataset, which is preprocessed, homogeneous, and has a balanced distribution. DenseNet-121, DenseNet-169, DenseNet-201, Inception-V3, MobileNet, MobileNetV2, NasNetMobile, and Xception CNN models were compared, and it was analyzed that the Xception model was more successful than other models in the Mpox classification task. The 204800 deep feature maps obtained from the layer just before the fully connected layer of the Xception model were reduced to 400 by Principal component analysis (PCA). Then, the deep feature maps were filtered with the Minimum Redundancy Maximum Relevance (mRMR) algorithm and passed through the feature selection process. After feature selection, the 100 feature maps obtained after the classification process of the Support Vector Machine (SVM) algorithm yielded an accuracy of 89.70%, precision of 89.69%, sensitivity of 89.70%, F1 of 89.66%, and specificity of 96.57%. The GridCVSearch method to optimize hyper-parameters was used, and a Repeated 5-fold cross-validation technique was used in all experimental studies. As a result of these results, our approach showed that it could increase the diagnostic accuracy rate of the disease, reduce the overall misdiagnosis rate, and be a potential alternative decision support system to traditional methods such as PCR and electron microscopy.

Keywords

Deep Learning , Convolutional neural network , Mpox , Monkeypox , Transfer learning , Classification

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