Accurate and early image analysis is crucial for proper diagnosis and treatment. Convolutional neural networks (CNNs) have the ability to precisely classify and segment the affected wound areas, which has revolutionized biomedical imaging for automatic detection and diagnosis. However, CNN models often face challenges like generalization and overfitting issues due to limited wound image data. In addition, RGB images are computationally intensive. They may prompt the model to focus on irrelevant features, such as color variations, instead of the most important ones, like silhouettes, ultimately leading the model to the overfitting problem. This study investigates the influence of color on ulcer semantic segmentation. We explore different color-to-grayscale conversion techniques to study the learning behavior of the CNN model. Traditionally, image conversion from an RGB color space to grayscale uses fixed transformation parameters, e.g., YUV color model weights, which are standardized for human observers. The YUV color model separates luminance (Y) from chrominance (U and V), allowing efficient brightness and color information representation. However, machine-based processing does not require ‘seeing’ the content but focuses on extracting the essential features for model training, data augmentation, or future design of task-specific sensors. We designed a universal model architecture to analyze the learning procedure’s ability to learn task-specific weights for color-to-grayscale conversion. The experiment results show how grayscale images based on learned weights could be used in task-specific semantic segmentation compared to color images and grayscale images obtained using traditional conversion techniques. The grayscale images based on learned weights are computationally efficient, and simplified feature representation helps the model emphasize the most relevant attributes while suppressing irrelevant ones. The source code is available on [1].