Background:

the accurate detection and classification of breast cancer through histopathological diagnosis is of vital importance for the effective treatment of the disease. Among the types of breast cancer, invasive ductal carcinoma (IDC) is the most common. Visual analysis of tissue samples under the microscope is a manual, time-consuming and observer-dependent process. However, in many countries, including Cuba, the use of software tools to assist diagnosis is scarce.

Objective:

to develop a software tool to detect IDC subtype breast cancer tissue in histopathological images.

Methods:

the tool is implemented in Python and includes IDC detection methods in histopathological images, based on algorithms for extraction of color and texture features in combination with a random forest classifier.

Results:

the open source tool provides a series of facilities for the reading, writing and visualization of histopathological images, automatic and manual delineation of cancer areas, management of patient diagnostic data and collaborative remote evaluation. It was evaluated in a database with 162 images of patients diagnosed with IDC, obtaining a balanced accuracy of 84 % and a F1 factor of 75 %.

Conclusions:

the tool allowed an interactive, fast, reproducible, precise and collaborative analysis through a simple and intuitive graphical interface. Future versions are expected to include new incremental machine learning methods for the analysis of digital histopathology images.

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