A novel feature reduction framework for digital mammogram image classification

Abstract

The visual similarity between normal breast tissues and abnormal lesions in digital mammogram images makes computer-aided diagnosis of breast cancer using automatically detected features a highly error-prone task. Our contribution in this paper is a novel feature reduction framework for selecting the most discriminative features that achieves both efficiency and classification accuracy. Our approach applies five individual feature-ranking methods including Fisher score, minimum redundancymaximum relevance, relief-f, sequential forward feature selection, and genetic algorithm for sorting the extracted features and selecting the features with highest ranking to setup a classifier. Our method achieves an accuracy of 94.27% and a sensitivity of 98.36% with a specificity of 99.27% on a set of 1,100 mammogram patches taken from image retrieval in medical applications database using a neural network classifier, which competes with state-ofthe-art classification accuracy 93.11%. Furthermore, we demonstrate that only 49 out of the 119 extracted features are sufficient to achieve the reported accuracy of normal vs. abnormal classification.

A Robust Feature Extraction Technique for Breast Cancer Detection using Digital Mammograms based on Advanced GLCM Approach

“…d. The performance of these methods is evaluated by confusion matrix parameters and misclassification rate. e. The results show that CLAHE+AGLCM with XGBoost is superior to previous works done by other authors [12] [13] [16]. The proposed methodology provides better mammogram classification using CLAHE+AGLCM.…”

“…Reference number Methodology Accuracy (%) [12] GLCM 94.2 [13] Gabor features 93.9 [16] CLAHE+HOG 66 [19] Spiculation index, fractional concavity and compactness 80%…”

“…The authors [12], Gray Level Co-occurrence Matrix (GLCM) was used for feature extraction. The feature selection techniques were applied and obtained 94.27% accuracy with a neural network classifier.…”