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This work focus on the detection of Monkeypox disease which has been declared a medical emergency by the World Health Organization. To carry out this work, a richly annotated dataset in YOLO and PASCAL VOC format has been created covering similar skin diseases such as, Monkeypox, Chickenpox, and Measles and further training and tests have been performed using deep learning-based algorithms. As an effort to propose a high-performing algorithm, an improved variant of the YOLO v3-tiny algorithm has been developed. The improvements in YOLO v3-tiny algorithm have been incorporated by employing the residual network (ResNet) and Spatial Pyramid Pooling (SPP) network. Furthermore, to reduce the computation cost of the developed algorithm, XNOR layers have been applied to the residual network. The proposed algorithm achieved a Mean Average Precisionvalue of 76.51% which was 4.54–48.88% higher compared to other tested baseline algorithms.

An XNOR-ResNet and spatial pyramid pooling-based YOLO v3-tiny algorithm for Monkeypox and similar skin disease detection