Psoriasis, eczema, deep learning, label smoothing, healthcare cyber-physical system, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

With psoriasis and eczema being the two most common diseases worldwide, achieving automatic diagnosis could be useful for healthcare cyber-physical system. However, creating such an automatic classification system is still challenging since it cannot learn positional and spatial information from unstable training. In this paper, we propose a novel abscissa-ordinate focused network (AOFNet) with active label smoothing for the identification of psoriasis and eczema from images. The AOFNet incorporates the developed abscissa-ordinate focused module that focuses on abscissa-ordinate information and leverages the attention mechanism to enhance the network’s ability to learn positional and spatial details, resulting in improved classification performance. Additionally, the adoption of an active label smoothing approach effectively mitigates the problem of overconfidence and effectively captures the dynamic changes that occur during training, thereby providing an added boost to the overall performance of the network. To evaluate the proposed healthcare cyber-physical system, extensive experiments are conducted on the clinical psoriasis and eczema dataset, and the results demonstrate that the designed system could gain comparable classification performance.