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Breast cancer (BC) is one of the leading causes of death in adult women worldwide and the best way to reduce mortality and improve prognosis is through early diagnosis. Thus, it is necessary to optimize diagnostic methods; one option could be the automatic detection of patterns in 1D-II. In that respect, through recent analysis of unidimensional Immunoblot Images (1D-II), it was possible to distinguish between women with and without breast disease using as a discrimination criterion the presence of autoantibodies (bands) in their blood. However, the analysis of 1D-II is a difficult task even for an expert, generating great subjectivity and complexity in the process of interpretation.

In the present study, a semi-automatic methodology for the bands’ analysis contained in the 1D-II’s was implemented and evaluated, the bands were extracted using digital image processing techniques. This was possible through the recognition of banding patterns represented as time series to distinguish between three classes: women with breast cancer (BC), women with benign breast pathology (BBP) and women without breast pathology (H). The classification was performed using the machine learning algorithm k-nearest neighbors (KNN) with different parameters over the time series representation.

The semi-automatic method here presented was able to reduce the time, complexity and subjectivity of the image analysis with the performance metrics compared, obtaining similar percentages for both representations. With the traditional analysis, binary representation [Accuracy 72.8%, Precision 73.42% for three classes (BC, BBP and H) and Accuracy 90.91% Accuracy 92.55% Sensitivity 93.57% and Specificity 92.99% for two classes (BC and H)], versus Time series representation [Accuracy 66.4%, Precision 67.07% for three classes (BC, BBP and H) and Accuracy 86.36% Accuracy 87.31% Sensitivity 95.86% and Specificity 85.56% for two classes (BC and H)].