Classifier ensemble generation and selection with multiple feature representations for classification applications in computer-aided detection and diagnosis on mammography |
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| Affiliation: | 1. Image and Video Systems Lab, Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Yuseong-Gu, Daejeon 305-701, Republic of Korea;2. Knowledge Media Design Institute, Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 3GA, Canada;1. Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Pokfulam, Hong Kong;2. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hunghom, Hong Kong;1. Department of Literature and Languages, Texas A&M University-Commerce, TX, USA;2. Department of Computer Science and Information Systems, Texas A&M University-Commerce, TX, USA;3. Department of Health Management and Informatics, University of Central Florida, FL, USAn;1. Division of Data Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam;2. Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam;3. College of Information and Communication Technology, Thai Nguyen University, Quyet Thang, Thai Nguyen, Vietnam;1. School of Engineering, University of British Columbia, 3333 University Way, Kelowna, BC, Canada V1V 1V7;2. AS Composite Inc., 835 Rue Bancroft, Pointe-Claire, QC, Canada H9R 4L6n;1. Department of Information Engineering and Computer Science, University of Trento, Via Sommarive, 9, I-38123 Trento, Italy;2. Department of Scientific Computing, Ain Shams University, Egypt;3. Vestec Inc. Canada and Department of Engineering Science, Suez University, Egypt;1. College of Business Administration, Hunan University, Changsha 410082, China;2. Center of Finance and Investment Management, Hunan University, Changsha 410082, China;3. Center for Polymer Studies and Department of Physics, Boston University, Boston, MA 02215, USA |
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| Abstract: | This paper presents a novel ensemble classifier framework for improved classification of mammographic lesions in Computer-aided Detection (CADe) and Diagnosis (CADx) systems. Compared to previously developed classification techniques in mammography, the main novelty of proposed method is twofold: (1) the “combined use” of different feature representations (of the same instance) and data resampling to generate more diverse and accurate base classifiers as ensemble members and (2) the incorporation of a novel “ensemble selection” mechanism to further maximize the overall classification performance. In addition, as opposed to conventional ensemble learning, our proposed ensemble framework has the advantage of working well with both weak and strong classifiers, extensively used in mammography CADe and/or CADx systems. Extensive experiments have been performed using benchmark mammogram dataset to test the proposed method on two classification applications: (1) false-positive (FP) reduction using classification between masses and normal tissues, and (2) diagnosis using classification between malignant and benign masses. Results showed promising results that the proposed method (area under the ROC curve (AUC) of 0.932 and 0.878, each obtained for the aforementioned two classification applications, respectively) impressively outperforms (by an order of magnitude) the most commonly used single neural network (AUC = 0.819 and AUC =0.754) and support vector machine (AUC = 0.849 and AUC = 0.773) based classification approaches. In addition, the feasibility of our method has been successfully demonstrated by comparing other state-of-the-art ensemble classification techniques such as Gentle AdaBoost and Random Forest learning algorithms. |
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