Image-Enhancement-for-Medical-Analysis-Using-Genetic-Algorithms

This Jupyter notebook contains the implementation of a basic Genetic Algorithm (GA) designed to enhance the contrast of RGB images in order to reveal finer details for medical analysis. The algorithm utilizes the sigmoid function for image processing and employs two distinct fitness functions—entropy and standard deviation—to evaluate and optimize the image enhancement process. The first section of the notebook contains the implementation of the basic Genetic Algorithm (GA), adapted to use the sigmoid function and the defined fitness functions. The second section features a function called encontrarmejor(), which performs a grid search to identify the optimal parameters with a reduced number of generations. It then executes the algorithm 10 times to evaluate its stability, displaying the results in a table. Additionally, this section shows a comparison of the image before and after the enhancement process.