A Decision Support System (DSS) for Breast Cancer Detection Based on Invariant Feature Extraction, Classification, and Retrieval of Masses of Mammographic Images

Abstract

This paper presents an integrated system for the breast cancer detection from mammograms based on automated mass detection, classification, and retrieval with a goal to support decision-making by retrieving and displaying the relevant past cases as well as predicting the images as benign or malignant. It is hypothesized that the proposed diagnostic aid would refresh the radiologist's mental memory to guide them to a precise diagnosis with concrete visualizations instead of only suggesting a second diagnosis like many other CAD systems. Towards achieving this goal, a Graph-Based Visual Saliency (GBVS) method is used for automatic mass detection, invariant features are extracted based on using Non-Subsampled Contourlet transform (NSCT) and eigenvalues of the Hessian matrix in a histogram of oriented gradients (HOG), and finally classification and retrieval are performed based on using Support Vector Machines (SVM) and Extreme Learning Machines (ELM), and a linear combination-based similarity fusion approach. The image retrieval and classification performances are evaluated and compared in the benchmark Digital Database for Screening Mammography (DDSM) of 2604 cases by using both the precision-recall and classification accuracies. Experimental results demonstrate the effectiveness of the proposed system and show the viability of a real-time clinical application.

An Automatic Breast Cancer Diagnostic System Based on Mammographic Images Using Convolutional Neural Network Classifier

“…Raza [11] used NN for detection of breast cancer and created a foundation of automated cancer detection so that diagnoses can be done in the early stages. From mammograms, a breast cancer (BC) detection system based on mass detection, retrieval, and classification performed by an automated system proposed by Rahman [12]. Which predicts images as benign or malignant.…”

Sınıflandırma Algoritmalarını Kullanarak Meme Dokusunda Kitleleri Değerlendirmeye Yönelik Karar Destek Sistemi

“…For this purpose, researchers have proposed many categorization strategy which are summarized as: methods using artificial neural networks (ANN) (Fernandes, A. S. , Alves, P. , Jarman, I. , Etchells, T. A. , Foncea, J. M. , Lisboa, P. J. G., 2010) (Baker, J. A. , Kornguth, P. J. , Lo, J. Y. , Williford, M. E. , Floyd, C. E., 1995), case-based reasoning methods via Hamming or Euclid distance (Floyd, C. E. , Lo, J. Y. , Tourassi, G. D., 2000) (Bilska-Wolak, A. O , Floyd, C. E., 2002) (Bilska-Wolak, A. O , Floyd, C. E., 2001), methods using Bayesian network (Markey, M. K. , Fischer, E. A. , Lo, J. Y. , 2004) (Jiang, X. , Wells, A. , Brufsky, A. , Neapolita, R. , 2019), decision tree (DT) (Mokhtar, S. A. , Elsayad, A. M., 2013) (Elter, M. , Schulz-Wendtland, R. , Wittenberg, T. , 2011), support vector machine (SVM) and extreme learning machines (ELM) (Rahman, M. , Alpaslan, N., 2017).…”

“…Also, to achieve a computer-aided diagnosis system (CAD) systems, a Graph-Based Visual Saliency (GBVS) technique is utilized for automated mass identification. Lastly, categorization and retrieval are operated via ELM, SVM, in addition to a linear combination-based similarity fusion method (Rahman, M. , Alpaslan, N., 2017).…”