Image Contrast Enhancement (ICE) is one of the most used techniques in image processing for image quality improvement at preprocessing stages; However, common ICE implementations like Histogram Equalization (HE) or Contrast-Limited Adaptive Histogram Equalization (CLAHE) often introduce noise or result in information loss, which negatively impacts overall image processing. This paper shows an implementation of recently introduced technique Agent-Search-based ICE (AS-ICE) which effectively reduces these undesired side effects. A comparative study shows how AS-ICE outperforms CLAHE in multiple image quality criteria. Furthermore, original images and images enhanced with CLAHE and AS-ICE are fed into a CV system for tuberculosis diagnosis, with AS-ICE images leading to the most accurate results, getting an improvement by reduce error classification from 28% to 0%.