Collagen remodeling is a hallmark of cancer progression and is critical for diagnosis and treatment planning. Second harmonic generation (SHG) microscopy, while effective for collagen imaging, is limited by high costs and the need for specialized equipment. In this study, we implemented a deep learning-based architecture to translate bright-field (BF) images of H&E-stained pancreatic tissue microarrays (TMAs) into the “collagen” images. The similarity analysis demonstrated that translated collagen images were equivalent to the SHG images. These translated images were subsequently used to extract collagen measures using first-order intensity-based texture analysis, which enabled the evaluation of morphological differences between tumor and normal pancreatic tissues. Statistical analysis was conducted to evaluate the significance of these collagen measures in distinguishing the classes. Furthermore, machine learning (ML) models were compared to identify the optimal model for differentiating the classes based on collagen measures. First-order intensity-based collagen measure analysis indicated “variance” as the most significant feature in classifying the two groups, which was in agreement with ROC-AUC (receiver operating characteristics–area under curve) analysis. The study indicated that the collagen features extracted from the “translated image” can be potentially used to understand the role of collagen remodeling in the progression of pancreatic tumors and can facilitate the analysis of stromal biomarkers.