This study addresses the implementation of artificial intelligence methods for assisted quantification of biomarkers in sporadic colorectal cancer. Accurate artificial intelligence models are crucial for medical applications such as diagnosis and prognosis of this major disease, which represents a growing cause of death worldwide and in Argentina. In low and middle-income countries such as our own, access to automatic image analysis platforms is limited due to their high cost. To address this, we are developing a workflow that explores automatic segmentation and a simplified immunological score to evaluate antibody expression in tumoral tissue images of sporadic colorectal cancer. The processes explored in the workflow include multiclass semantic segmentation with U-Net neural network, correlation between biomarkers CD3, CD8, CD146 and PDGF-Rβ, and patient data analysis using machine learning models. Results indicated that the U-Net transfer learning model achieved the highest precision in segmenting the tumor core and invasive margin regions with intersection over union scores of 0.94 and 0.88 respectively. However reduced performance was noted in underrepresented regions, emphasizing the necessity for improved data balance. From the Pearson coefficient, a pattern of strong correlations was observed between the biomarkers and their expression within the tumor core. Random Forest analysis of the patient data evidenced the three most important features: TMN 18.48%, CD146 16.20% and CD8 12.24%. Our goal is to establish an analysis workflow that streamlines research efforts by facilitating the characterization of new potential biomarkers and, upon validation and implementation, enhances diagnostic processes for pathologists.