Automated breast cancer detection in digital mammograms: A moth flame optimization based ELM approach

Abstract

Early detection of breast cancer based on a digital mammogram is an important research domain in the field of medical image analysis. An improved CAD model is proposed in this paper for the classification of breast masses into the normal or abnormal and benign or malignant category. The proposed model utilizes lifting wavelet transform (LWT) to extract the features from the region of interest mammogram images. The dimension of the feature vectors is then reduced by using a fusion of PCA and LDA methods. Finally, the classification is performed using a combination of an extreme learning machine and moth flame optimization technique (MFO-ELM). In the MFO-ELM algorithm, MFO is used to optimize the hidden node parameters of ELM. Further, 5-fold stratified cross-validation is used to improve the generalization performance of the model. The proposed model is evaluated on two standard datasets, namely MIAS and DDSM. From the experiment, it is observed that the proposed CAD model obtains ideal results for the MIAS dataset and achieves an accuracy of 99.76% (normal vs. abnormal) and 98.80% (benign vs. malignant) for the DDSM dataset. Our proposed model also demands minimum computational time as compared to other existing models. The experimental results show that the proposed model is superior to other state-of-the-art models in terms of classification accuracy with a significantly reduced number of features.