Morphological and textural descriptors analysis of digital mammograms with radiological findings to support breast cancer detection using artificial neural networks

<i>Objective</i>. To classify digital mammograms based on radiological findings using morphology and texture descriptors with artificial neural networks (ANN) for breast cancer detection.<i>Approach.</i>The mammography dataset from High Specialty Regional Hospital of Oaxaca (HRAEO) (median patient age (mpa), 48 years [interquartile range (IQR), 41-54 years]) with radiological findings was retrospectively analyzed. All patients underwent breast biopsy and were not previously treated. External testing was performed using mammograms from the National Cancer Institute (INCAN) (mpa: 47 years [IQR, 37-62 years]). The morphology was analyzed using a circularity descriptor (<i>к</i>), and the texture was analyzed using the mean height/width ratio of the extrema descriptor (<i>ρ</i>). These results were compared with cancer/benign histopathology, which was binarily classified using ANNs. The F1-score, Cohen's kappa (K), and area under the ROC curve (AUC) were employed as evaluation metrics, and the Wilcoxon rank-sum test was used for statistical analysis (h = 0, with p > 0.05, was considered as not statistically significant).<i>Main results.</i>216 raw mammograms from HRAEO and 33 mammograms from INCAN (95 + 16 breast cancer and 121 + 17 benign findings) were included. The best internal testing results were obtained with a one-hidden-layer ANN with 100 neurons, achieving a F1-score of 0.95, K of 0.91, and an AUC of 0.953 (95% confidence interval [CI]: 0.917, 0.977) (h = 0, p > 0.99). However, the external testing results were significantly lower: 0.38 F1-score, 0.02 K, and 0.509 AUC (95% CI: 0.344, 0.664) (h = 0, p = 0.14) due to not exactly meeting the inclusion criteria and possible demographic and spectrum bias, or domain-adaptation issues.<i>Significance</i>. The proposed morphology (<i>к</i>) and texture (<i>ρ</i>) descriptors show promise for detecting breast cancer in raw mammograms, with radiological findings, in a local context. However, their poor external performance highlights the need for substantial further work before this approach can be deemed suitable for broader diagnostic applications.