Classification of breast tumours on ultrasound images using morphometric parameters

This work aims to assess the potentiality of morphometric parameters in separating breast tumour, on ultrasonic images, as malign or benign. Parameters were calculated over normalised radial length and convex polygons from 152 segmented tumour images. Linear discriminant analysis was applied and parameters performance assessed (accuracy, sensitivity and specificity). The best parameter performances for individual parameters were the normalised residual mean square value and the circularity. Taking these last two and the roughness the best separation performance was obtained: specificity (90.4%) and sensitivity (88.0%). These three parameters were also applied to a multilayer perceptron network using GA-backpropagation hybrid training. The initial results pointed out that hybrid GA-backpropagation training was capable to produce similar high performance both to training (accuracy = 90.3%, sensitivity = 90.0% and specificity = 90.9%) and test (accuracy, sensitivity and specificity equal to 90.0%) procedures. Besides, the performances obtained with two training sets of distinct sizes (30% and 50% of all samples) were slightly different.