Combining CNNs and Symptom Data for Improved Monkeypox Virus Detection

The zoonotic disease known as monkeypox, which is related to smallpox, can be difficult to diagnose since it resembles other illnesses that might cause the same symptoms. In this study, we present an accurate technique for monkeypox detection based on convolutional neural networks (CNNs). Our method improves diagnosis reliability by having the CNN model incorporate a dataset of nine important symptoms. For creating CNN-based monkeypox detection system, the researchers used a collection of high-definition photos and nine symptoms. The model was constructed using TensorFlow's Keras API and Python 3.10, classifying lesions based on a probabilistic score. A likelihood greater than 50% on CNN indicated that the case was really monkeypox. If not, five or more symptoms suggested that monkeypox may be present with metrics including accuracy, precision, recall, and F1-score. The study developed a CNN model to predict monkeypox from lesion images and symptom analysis. The model, built with layers including convolutional, pooling, and fully connected, achieved high accuracy and low loss, with metrics like ROC AUC (0.84), precision (0.78), and recall (0.90). The model effectively differentiates between monkeypox and other conditions. Tables illustrate the model's predictions based on various symptom combinations, highlighting its capability to assess monkeypox risk with high confidence. The study developed a CNN model integrating patient symptoms and skin lesion images for accurate monkeypox diagnosis. With a balanced accuracy of 82%, F1-Score of 84%, and sensitivity and specificity of 80% and 85%, respectively, the model effectively distinguishes monkeypox from similar illnesses. It demonstrated resilience despite limited high-resolution images, emphasizing the potential of CNNs in medical diagnosis. The model's performance can be further improved by incorporating additional data and advanced techniques, enhancing early detection in future outbreaks.