Mass classification in breast DCE-MR images using an artificial neural network trained via a bee colony optimization algorithm

Abstract

Breast cancer is becoming the leading cause of cancer deaths among women. The best way to reduce deaths due to breast cancer is early detection and treatment. Dynamic contrast enhanced (DCE) MRI has emerged as a promising new imaging modality for breast cancer screening. Currently, radiologists evaluate breast lesions based on a qualitative description of lesion morphology and contrast uptake profiles. The qualitative description of breast lesions from DCE-MRI introduces a high degree of inter-observer variability. In addition, the high sensitivity of MRI results in good specificity. A computer-assisted evaluation system that can automatically analyse lesion features to differentiate between malignant and benign lesions would be very useful. One of the major characteristics for mass classification is texture. Artificial neural networks exploit this important factor to classify the mass as benign or malignant. The selected texture features were used to classify the mass with a three-layered neural network to predict the outcome of a biopsy. The main objective of this proposed method is to increase the effectiveness, robustness, and efficiency of the classification process in an objective manner to reduce the numbers of false-positive results. The paper presents an intelligent computer assisted mass classification method for breast DCE-MR images. It uses the artificial bee colony algorithm to optimize the a neural network performing benignmalignant classification on the region of interest. A three-layer neural network with seven features was used for classifying the region of interest as benign or malignant. The network was trained and tested using the artificial bee colony algorithm and was found to yield a good diagnostic accuracy.

Experimental Investigation of Classification Algorithms for Predicting Lesion Type on Breast DCE-MR Images

“…The neural network classifiers are designed and their structure is individually optimized by an artificial bee colony algorithm based on the work proposed in [29]. The classifier chosen is a multilayer feed forward neural network [19].…”

Exploring Breast Cancer Texture Analysis through Multilayer Neural Networks

“…ANN-PSO (artificial neural network with particle swarm optimization) method has been used by Zhang et al to extract the intensity and shape features, which tested and then distinguished normal and abnormal breast mammograms [18]. The particle swarm optimized wavelet neural network (PSOWNN) technique was applied to breast mammograms by Sathyaa et al and the Receiver Operating Characteristic (ROC) curve indicated the sensitivity and specificity as indicated by Dheeba et al [19,20].…”