AI-based monkeypox detection model using Raspberry Pi 5 AI Kit

Abstract

Monkeypox is a zoonotic disease that originated from monkeys and then spread to humans; this disease recently popped up globally with increased risks of spreading from human to human and clinical presentation similar to other pox-like diseases. Quick and right identification is fundamental for containment and treatment that will minimize the spread of the disease. The current conventional diagnostic techniques include PCR which takes time, and money, and often needs sophisticated laboratories that cannot be easily accessed in developing countries. This work describes the creation and application of a monkeypox detection algorithm orchestrated on the Raspberry Pi 5 AI Kit. Developed based on convolutional neural networks (CNNs), the model enables one to distinguish actual monkeypox lesions in the images. The Raspberry Pi 5 AI Kit allows for edge computing solutions to be implemented, making the entire solution mobile, affordable, and perfect for locations with low connectivity. Extensive data collection and data preprocessing were performed, and the final dataset with monkeypox and skin lesion images consisted of more than 5000 verified images. 94% accuracy was obtained by the model, making it superior to the model available in literature. The implementation proves that powerful AI technologies can be applied to low-cost hardware to become a valuable weapon in the monkeypox frontline workers’ arsenal and advance the efforts against monkeypox infections.