Breast cancer is a fatal disease causing high mortality in women. Constant efforts are
being made for creating more efficient techniques for early and accurate diagnosis.
Classical methods require oncologists to examine the breast lesions for detection and
classification of various stages of cancer. Such manual attempts are time consuming and
inefficient in many cases. Hence, there is a need for efficient methods that diagnoses the
cancerous cells without human involvement with high accuracies. In this research,
image processing techniques were used to develop imaging biomarkers through mammography
analysis and based on artificial intelligence technology aiming to detect breast
cancer in early stages to support diagnosis and prioritization of high-risk patients. For
automatic classification of breast cancer on mammograms, a generalized regression
artificial neural network was trained and tested to separate malignant and benign
tumors reaching an accuracy of 95.83%. With the biomarker and trained neural net, a
computer-aided diagnosis system is being designed. The results obtained show that
generalized regression artificial neural network is a promising and robust system for
breast cancer detection. The Laboratorio de Innovacion y Desarrollo Tecnologico en
Inteligencia Artificial is seeking collaboration with research groups interested in validating
the technology being developed.