Automated diagnosis of breast cancer using parameter optimized kernel extreme learning machine. (September 2020) - Mohanty, Figlu; Rup, Suvendu; Dash, Bodhisattva
Highlights: Present model aims at an effective cancer classification using digital mammograms. A block-based CDTM approach is employed to extract the features. A parameter optimized KELM is proposed using the concept of grasshopper optimization. The optimization is performed for optimal selection of features and KELM parameters. The model shows superior performance for binary as well as multiclass classification. Abstract: Early detection and diagnosis is one of the primary steps towards an effective treatment of breast cancer. An automated and reliable computer-aided diagnosis (CAD) model is a suitable alternative to facilitate and ease the diagnosis process. The proposed work focuses on developing an efficacious CAD model that can classify the digital mammograms as normal, benign, or malignant. First, a block-based cross diagonal texture matrix (CDTM) technique is applied to the mammogram ROIs, and then Haralick's features are extracted from each of the ROIs. Then, the kernel principal component analysis (KPCA) technique is used to reduce the dimension of the generated feature vector. Finally, a wrapper-based parameter optimized kernel extreme learning machine (KELM) is proposed wherein the principle of the grasshopper optimization is utilized to determine the optimal values of KELM parameters. Additionally, the proposed wrapper technique is also used to select the important features from the reduced feature vector. The proposed scheme is compared with some of the recently reported CAD models on two standard and publicly available datasets, namely, MIAS and DDSM. From experimental results and analysis, it is noticed that the proposed CAD model yields better results in terms of classification accuracy, and area under curve with a significantly reduced number of features, as compared to that of its counterparts. Additionally, the model is also validated for multi-class classification on the aforementioned datasets reporting an accuracy of 97.49% and 92.61% for MIAS and DDSM datasets, respectively. Further, the proposed model demands minimum computational time than that of its counterparts.