A neural network approach for contrast enhancement image

Tuberculosis infection is a serious disease which could be controlled by early diagnosis. A commonly used technique for detecting the TB bacilli is by analyzing sputum smear. Now days, image recognition systems have several applications in enormous fields. This paper uses an artificial neural network to enhance color images of Ziehl-Neelsen stained smear for the purpose of detecting TB bacilli. The first necessary step is the captured images are converted into usable format (RGB values) and pass the RGB values to neural network for training to emulate the contrast enhancement technique. The training is based on back-propagation algorithm. It is found that the proposed neural network approach could emulate contrast enhancement technique quite well.