Hyperspectral imaging (HSI) which captures a wide spectrum of light has emerged as a tool for the detection and diagnosis of various medical conditions. However, due to the high cost of specialized HS cameras, it is limited in its use in clinical settings. In this research, a comprehensive comparison is carried out between two architectures for hyperspectral reconstruction algorithms for medical images of acne vulgaris. The evaluation will consist of an analysis of different hyperparameter configurations to identify the optimal reconstruction algorithm for medical hyperspectral images. The results show that the HRNET architecture model, which includes colour correction, random cropping, and a small batch size had the lowest mean relative absolute error of 0.0433. Therefore, the reconstructed hyperspectral (HS) images using HRNET architecture could offer a viable and cost-effective alternative to utilizing expensive hyperspectral imaging (HSI) equipment for detecting medical conditions.