Breast cancer diagnosis based on spiculation feature and neural network techniques

Abstract

The degree of spiculation of the tumor edge is a particularly relevant indicator of malignancy in the analysis of breast tumoral masses. This paper introduces four new methods for extracting the spiculation feature of a detected breast lesion on mammography by segmenting the contour of the lesion in a number of regions which are separately analysed, determining a characterizing spiculation feature set. In order to differentiate between benign and malignant tumors based on the extracted spiculation sets, an intelligent neural network is first trained on a number of 96 cases of known breast cancer malignancy and then tested for diagnosing and classifying breast cancer tumors. The input of the neural network is thus the extracted spiculation feature set and the output is represented by the histopatological diagnostic given by doctors. Finally, the performance of the introduced methods is analysed depending on the number of regions in which the contour is segmented and the performance-related conclusions are stated for each of the methods. The highlight of this paper is the division of the tumour contour in regions and the assessment of a spiculation indicator for each region, resulting a set of spiculation indicators that characterise the tumour and -by training a neural network -can be used in classifying breast tumours with high performance.

Using Multilayer Perceptron and Deep Neural Networks for the Diagnosis of Breast Cancer Classification

“…In the study, the error of the model comes out to be as low as 1% for type I (classifying benign samples falsely as malignant or classifying true instance as false) and 0.5% for type II (classifying malignant sample falsely as benign or classifying false instance as true). [13] introduces four new methods for extracting the speculation features of a detected breast lesion on mammography by segmenting the contour of the lesion in a number of regions which are separately analyzed, determining a characterizing speculation feature set using neural network. In the paper, the performance of the methods is analyzed depending on the number of regions in which the contour is segmented and the performance related conclusions are stated for each of the methods.…”