The sentinel lymph node is a predictor of breast cancer aggressiveness.The hazard
ratio reported was 2.14, 95% confidence interval, shows that the patient with micro-metastases
(MM) have higher probability of poorer Disease-free survival (DFS) and overall survival
(OS) relative to those who are node-negative, therefore early detection of micro-metastasis
analysis appears to be the approach most advantageous for patients. 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 . This description is complemented by the cellular
area obtained from a conventional watershed segmentation. An AdaBoost model, trained
with 300 patches of 350 x 350 pixels (56000 mu m(2)) 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 En-score
of 0.77 for exactly the same task.
