Diagnosis of Breast Regions through the Use of Ripley's K Function and SVM

Simara V. da Rocha, Geraldo Braz Jr., Anselmo C. de Paiva, and Aristófanes C. Silva


Pattern Recognition, Breast Cancer Diagnosis, Ripley's K Function


Breast cancer has become increasingly common among the female population over 40 years and is the type of cancer that affects more women worldwide. One way to early detect non-palpable tumors that cause breast cancer is to perform an X-ray (mammogram) of the breasts. It is known that the chances of curing breast cancer is high if detected in early stages. However, the sensitivity of this test may vary widely due to factors such as examination quality or specialist experience. Thus, the use of diagnose systems in order to assist the specialist, have increased the chances of correct diagnoses. This paper presents a methodology for image spatial texture analysis and recognition of patterns present in mass extracted from images of mammograms, according to their malignant or benign behavior. Therefore, this paper uses the Ripley's K function to extract texture and SVM for pattern recognition. The best result achieved was 85% accuracy, 88.23% sensitivity and 80.76% specificity with Az = 0.84.

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