The sentinel lymph node is a predictor of breast cancer aggressiveness.¹ Patients with micro-metastasis (MM) are usually considered negative, yet their the hazard ratio has been reported to be 2.4 and 1.203.81 with a 95% confidence interval.^2–4 This work proposes an automatic detection of micro-metastasis by quantifying local cellular changes. The proposed strategy characterizes nuclei morphometry, color and texture to establish differences between MM and normal tissue. The color model is obtained from the plane [(r − b), g] while texture corresponds to the Haralick's features from five different orders of the co-occurrence matrix.⁵ This description is complemented by the cellular area obtained from a conventional watershed segmentation. An AdaBoost model, trained with 300 patches of 350 × 350 pixels (56000 μm² ) randomly selected from 18 cases, was tested in a set of five different cases with approximately ten patches containing micro-metastasis. This approach obtained a best classification accuracy of 0.86, sensitivity of 0.89, specificity of 0, 83, and F-score of 0.86, while the baseline, a ResNet 50 model, obtained 0.74 of accuracy, 0.86 of sensitivity, 0, 63 of specificity, and F-score of 0.77 for exactly the same task.