Detection of Monkey – Pox Disease Using State of art Deep Learning Tecniques

Abstract

The viral infection known as Monkey-pox has recently shown a significant increase in its spread. Diagnosing the disease poses a challenge for experts due to its resemblance to other illnesses, particularly those related to smallpox. This study contributes to the advancement of Monkey-pox disease detection through the introduction of a highly effective deep-learning methodology. We present an innovative method employing deep learning algorithms to accurately and efficiently identify pox diseases using image processing techniques. We evaluate several deep learning CNN architectures (ResNet50, InceptionV3, DenseNet121, and EfficientNetB2) based on their accuracy and computational efficiency. Employing data augmentation techniques enhances model generalization and diminishes overfitting, enabling robust feature learning. Our findings emphasize DenseNet121's superiority, achieving an outstanding 93.18% accuracy. DenseNet121 consistently demonstrates superior confidence rates and accuracy across disease categories, showcasing its reliability in pox disease identification. Moreover, DenseNet121 exhibits faster prediction times compared to other models, enhancing its suitability for practical implementation.