Usage of Particle Swarm Optimization in Digital Images Selection for Monkeypox Virus Prediction and Diagnosis

Abstract

Identifying skin diseases by using digital images of skin that are also automated, efficient, and accurate is essential for biomedical image analysis. Many researchers have developed numerous machine-learning techniques for the prediction and diagnosis of various diseases that help clinicians identify infections early and provide crucial data for virus management. In this work, we use the inherent attributes of PSO, such as exploration and exploitation, to identify images for monkeypox virus prediction and diagnosis. Alongside, monkeypox, chickenpox, smallpox, cowpox, measles, tomato flu, and normal skin images were all considered in this study for monkeypox virus prediction and diagnosis. We collect photos from the International Skin Imaging Collaboration (ISIC) for analysis and experimentation purposes. Finally, we compare the proposed model PSOMPX for monkeypox virus identification with four distinct pre-trained deep learning models (e.g., VGG16 [29], ResNet50 [29], InceptionV3 [31], and Ensemble [30]), and the classifier hidden Markov model along with the GLCM-SVM is used in the diagnostic test to separate monkeypox skin lesions from other skin infections. The four performance evaluation metrics—accuracy, precision, recall, and F1 score—evaluate the model and analyze the outcomes of experiments. Finally, the experimental results obtained through the PSOMPX model significantly outperformed other models due to its numerous traits, with a total accuracy of 90.01% (F1-Score: 85.87%) achieved.

Image-based Skin Disease Detection and Classification through Bioinspired Machine Learning Approaches

“…Ultimately, classification is performed through the GLCM-SVM classifier. The study employs a dataset of 700 skin images depicting diverse dermatological conditions [37][38][39], and the experimental findings are represented in the confusion matrix depicted in Figure 6. The suggested method detects distinct skin diseases.…”

“…The datasets for the research undertaken in the paper is compiled by downloading images of infected skins images from various websites (examples: ISIC [38], other news sources in Kerala and Odisha, India [39]). The collection has 700 images to represent each skin diseases (monkeypox, chickenpox, smallpox, cowpox, and measles, and others from various websites [37][38][39]. A few sample images of the dataset thus prepared are shown in Figure 2.…”