From a diagnostic perspective, image enhancement has diverse potential in image processing applications related to biomedical images. A hybrid algorithm obtained by combining discrete wavelet transformation with soft computing techniques is proposed for enhancing the biomedical images. This paper proposes an approach for effective visual enhancement of biomedical images. The proposed approach uses scale-invariant feature transform algorithm and principal component analysis as pre-enhancement steps, followed by the combination of DWT and the genetic algorithm to enhance the biomedical images. In GA, a new fitness function, which can efficiently reduce the noise in biomedical images while preserving the details, is proposed for the enhancement process. In order to accurately evaluate the enhanced image's quality, various metrics like peak signal to noise ratio, contrast to noise ratio, BETA coefficient, standard deviation, and mean square error have been considered. Finally, the comparison of the proposed algorithm with other soft computing techniques like Bacterial Foraging, Particle Swarm Optimization and Fuzzy Logic is carried out. The results show that the proposed technique outperformed over the other methods and provided better image quality.