Diffuse reflectance spectroscopy exploits multiple scattering and absorption of light in superficial tissue to capture characteristic spectral signatures. When combined with machine learning, this approach enables accurate, real-time, non-invasive detection. Conventional drug screening methods, based on biochemical assays of urine, blood, or hair are limited by their invasiveness, long turnaround time, high cost, and poor portability that hinder their use in rapid, large-scale applications. To address these limitations, we propose a fast, efficient, and high-accuracy non-invasive drug screening framework that integrates diffuse reflectance spectroscopy with sex-stratified machine learning models. A skin spectral trace element database was established using data collected from over two thousand individuals. Two sequential classification pipelines were developed: support vector machines for male subjects and an Extreme Gradient Boosting for female subjects. In a case study focused on heroin detection, both models achieved high classification accuracy and demonstrated strong performance in terms of the area under the receiver operating curve. This approach provides a practical solution for preliminary drug screening and demonstrates the promise of combining optical spectroscopy with artificial intelligence for real-world drug surveillance.

Robustly computing the skeletons of objects in natural images is difficult due to the large variations in shape boundaries and the large amount of noise in the images. Inspired by recent findings in neuroscience, we propose the Skeleton Filter, which is a novel model for skeleton extraction from natural images. The Skeleton Filter consists of a pair of oppositely oriented Gabor-like filters; by applying the Skeleton Filter in various orientations to an image at multiple resolutions and fusing the results, our system can robustly extract the skeleton even under highly noisy conditions. We evaluate the performance of our approach using challenging noisy text datasets and demonstrate that our pipeline realizes state-of-the-art performance for extracting the text skeleton. Moreover, the presence of Gabor filters in the human visual system and the simple architecture of the Skeleton Filter can help explain the strong capabilities of humans in perceiving skeletons of objects, even under dramatically noisy conditions.