A computer-aided diagnosis system using Tchebichef features and improved grey wolf optimized extreme learning machine

Early detection is a key step for effective treatment of breast cancer and computer-aided diagnosis (CAD) is the most common tool used by the medical research community to detect early breast cancer development. Automated and accurate classification of mammogram images is an important criterion for the analysis and interpretation of these images and many methods have been proposed in this direction. In this paper, an improved CAD model is developed to classify the digital mammograms into normal and abnormal, and further, benign and malignant. The proposed model constitutes four different phases, namely, region of interest (ROI) generation, feature extraction, feature reduction, and classification. The proposed model first employs discrete Tchebichef transform (DTT) to extract the features from the ROIs. Subsequently, a technique based on a combination of principal component analysis (PCA) and linear discriminant analysis (LDA) is employed to reduce the dimensions of the feature vector. Next, the reduced features are sent to an extreme learning machine (ELM) for the classification. Here, to obtain a better generalization performance, the hidden node parameters of ELM are optimized through an improved grey wolf optimization-based ELM (IGWO-ELM). To validate the proposed CAD system, different performance metrics such as accuracy, sensitivity, specificity, and area under curve (AUC) are measured using k-fold stratified cross-validation (SCV). Moreover, to eliminate the issue of randomness, 10 independent runs are carried out on SCV. From a detailed analysis of the results, it is observed that the proposed model yields an average accuracy of 100% for MIAS dataset in both normal vs. abnormal, and benign vs. malignant cases. Further, the accuracy achieved for DDSM dataset is 99.50%, and 98.50% for normal vs. abnormal, and benign vs. malignant cases, respectively. The computation time taken by the proposed CAD model for MIAS and DDSM are 1.131 secs and 3.063 secs, respectively. The experimental analysis justifies the effectiveness of the proposed CAD model and as a result, this model can be considered as an effective tool to help the radiologists for better diagnosis.