This study looks at how well a Support Vector Machine (SVM) with a quadratic polynomial kernel works for detecting Monkeypox. The SVM method is compared to other machine learning models like Neural Networks, KNN, Logistic Regression, Random Forest, Decision Tree, and Naïve Bayes. By using features from medical images called Local Binary Patterns (LBP), the SVM model showed the best results, with 93.33% accuracy, 95.24% recall, 91.67% true negative rate, and 90.91% precision. The LBP features are used because they exhibit unique textural patterns that can distinguish Monkeypox and normal cases. The results show that the SVM with this kernel is good at telling the difference between Monkeypox and normal cases, making it a helpful tool for early detection in healthcare.

This paper investigates the application of EfficientNetV2, an advanced variant of EfficientNet, in diabetic retinopathy (DR) detection, a critical area in medical image analysis. Despite the extensive use of deep learning models in this domain, EfficientNetV2’s potential remains largely unexplored. The study conducts comprehensive experiments, comparing EfficientNetV2 with established models like AlexNet, GoogleNet, and various ResNet architectures. A dataset of 3662 images was used to train the models. Results indicate that EfficientNetV2 achieves competitive performance, particularly excelling in sensitivity, a crucial metric in medical image classification. With a high area under the curve (AUC) value of 98.16%, EfficientNetV2 demonstrates robust discriminatory ability. These findings underscore its potential as an effective tool for DR diagnosis, suggesting broader applicability in medical image analysis. Moreover, EfficientNetV2 contains more layers than AlexNet, GoogleNet, and ResNet architecture, which makes EfficientNetV2 the superior deep learning model for DR detection. Future research could focus on optimizing the model for specific clinical contexts and validating its real-world effectiveness through large-scale clinical trials.