Classifying similar disease characteristics can be challenging, especially when multiple classes are involved. Classifying conditions with multiple classes is riskier. Early and accurate disease detection enables the physician to treat the case appropriately. In a real-world scenario, classifying a similar type of disease is essential. Recently, researchers implemented deep learning approaches to classify images of the pox virus. Pre-trained models are commonly used to classify the disease. However, medical images contain significant noise and uncertain information, complicating the classification process. Different deep learning approaches such as Convolution Neural Network (CNN), Visual Geometry Group (VGG16), and Resnet50 were applied for the classification of pox virus images. In this research work, VGG16 produces better results in generalization. So, the VGG16 method is taken for improvement. The VGG-16 approach often leads to overfitting and incorrect data classification. To solve this problem, we incorporated two primary functions into the VGG16 models, which are as follows: Initially, we employed fuzzy functions to manage uncertainty and Principal Component Analysis (PCA) to reduce dimensionality. We compare the hybrid FuzzyPCA-VGG16 model with other benchmark methods for classification, this method minimizes the overfitting. The experimental results show a significant improvement over other model. The hybrid FuzzyPCA-VGG16 method attains 97.14% accuracy in binary classification and 91.42% in multi-class classification. The proposed work significantly improves the classification report for both binary and multi-class classification. This approach supports early and accurate disease classification.

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