LIMD: LBP-InceptionV3 Hybrid Approach for Mpox Detection Using Skin Lesion Images

Mpox skin lesions may exhibit visual similarities with other infectious skin conditions, particularly chickenpox and measles, making reliable image-based differentiation challenging. This study presents an LBP InceptionV3 hybrid approach for Mpox detection using skin lesion images. The proposed framework combines high level visual representations extracted using pretrained InceptionV3 with complementary local texture information generated using Local Binary Pattern (LBP). The secondary dataset comprised 228 original images. 102 images belong to Mpox class, while 126 images belong to the others class (chickenpox and measles). The images were expanded through augmentation to 3,192 images comprising 1,428 Mpox and 1,764 Other images. Images were standardized to 224 × 224 pixels and divided into training, validation, and testing sets using a 70:15:15 ratio. InceptionV3 generated a 2,048-dimensional deep feature representation, while LBP texture maps were processed through a shallow convolutional network to obtain 512 texture features. The resulting 2,560-dimensional representation was fused for classification. The proposed model achieved 94.15% accuracy, 94.16% precision, 94.15% recall, 94.16% F1 score, and an AUC of 0.99. On the 479 image test set, 451 images were correctly classified, including 202 Mpox and 249 Other images. The ablation study demonstrated that incorporating LBP improved accuracy from 88.72% for InceptionV3 alone to 94.15%, representing a 5.43 percentage point improvement, while AUC increased from 0.95 to 0.99. In addition, clinically sourced images from Owerri General Hospital were used to provide an independent assessment of the model on previously unseen skin lesion samples. The findings demonstrate that combining deep visual and local texture representations provides an effective approach for Mpox skin lesion classification.