Histopathological image classification in Tumor Microarrays (TMAs) is a crucial component in modern oncopathology and personalized medicine. This study proposes a fuzzy set-based context-aware decision framework to enhance classification accuracy by incorporating contextual features such as image dimensions and tissue morphology indicators. Utilizing a dataset of 538 labeled histopathological images across five tumor classes (High-Grade Serous Carcinoma (HGSC), Endometrioid Carcinoma (EC), Clear Cell Carcinoma (CC), Low-Grade Serous Carcinoma (LGSC), and Mucinous Carcinoma (MC)), we developed a hybrid model that integrates fuzzy logic with decision theory and econometric tools. Our framework employs rule-based fuzzy inference systems, context attribute clustering, and performance evaluation using confusion matrices and precision-recall metrics. Econometric regression was performed to determine the influence of contextual features like image width and height on classification accuracy. Results revealed significant differences in class representation and spatial resolution, which were found to influence classifier confidence. The fuzzy system achieved a macro-average F1 score of 0.81, outperforming traditional models in low-data-class scenarios. This work demonstrates the viability of fuzzy logic in clinical image analysis, offering a promising decision support tool for pathologists and data scientists in biomedical diagnostics.