The work projected in this research introduces a novel approach towards interpretable machine learning systems with the development of an evolutionary algorithm-based concept refinement pipeline for handwritten digit classification. The work presents an important obstacle in Explainable Artificial Intelligence (XAI) in automatically discovering optimal granularities for interpretable concepts while actually maintaining classification performance. The foundation of the pipeline relies on three important components: visual concept annotator, concept bottleneck model and evolutionary concept generator model. The visual concept annotator transforms raw MNIST digit images into binary concept annotations for five fundamental visual concepts (loops, vertical lines, horizontal lines, diagonal lines and curves) using tailored computer vision techniques that include the calculation of convexity for loop detection, Sobel transformation for vertical and horizontal lines, Hough line transformations for diagonal lines and curvature analysis in order to identify any curves.