Computational Intelligence in Patient-Sensitive Medical

Computational Intelligence in Patient-Sensitive Medical Decision Systems Report from Walter Karplus Summer Research Grant awarded by IEEE Computational Intelligence Society

Summer 2007

Maciej A. Mazurowski1

Academic Advisors: Jacek M. Zurada1 and Georgia D. Tourassi2

1 Computational Intelligence Laboratory Electrical and Computer Engineering Department University of Louisville 2 Duke Advanced Imaging Laboratories Duke University Medical Center

Abstract This report summarizes the work sponsored by IEEE Walter Karplus Summer Research grant awarded in 2007 by IEEE Computational Intelligence Society. Within this project a cooperative research was performed at the Computational Intelligence Laboratory at the University of Louisville and Duke Advanced Imaging Laboratories at Duke University Medical Center. In the project, ensemble techniques were applied to improve performance of a previously presented computer-aided detection (CAD) classiﬁer. The classiﬁer was designed for detection of masses in mammograms. As a result of the project, a paper was written and presented at SPIE Medical Imaging 2008.

1 Introduction and motivations

Computer-aided decision (CAD) systems have gained interest and popularity in medical diagnosis. CAD systems provide a second opinion regarding patients diagnosis based on previously acquired clinical information. Among other tasks, CAD systems have been thus far successfully applied in breast, lung and colon cancer detection [1, 2, 3, 4, 5]. Some of the presented systems utilize computational intelligence techniques [6, 7, 8, 9, 10] Recently, some efforts have been made to incorporate ensemble techniques to CAD systems [11, 12, 13, 14, 15, 16]. In this project, we have made further investigations on this topic. In our studies we used a modular approach where the cases were initially divided into separate subgroups. Then, classifiers based on each of these groups served as experts in the classification task. After several sub-classifiers were constructed, a combiner was trained to fuse the particular decisions in one final response.

1 2 Multidisciplinary nature of the project

The implemented project had a highly multidisciplinary character. It involved knowledge from the ﬁelds of computational and artiﬁcial intelligence, database management and medical diagnosis. Following this requirement, the project involved the collaboration of scientists from different domains. Maciej Mazurowski and Dr. Jacek Zurada (Computational Intelligence Laboratory, University of Louisville) were responsible for the development and application of computational intelligence techniques. Dr. Georgia Tourassi (Department of Radiology, Duke University Medical Center) provided her expertise on the medical imaging and medical decision aspects of the project. I have visited the associated University (Duke) twice for a period of 2 months total. The visit allowed for a better exposure to the medical imaging aspects of CAD systems development. The time spent at Duke University also strengthened the cooperation between the Computational Intelligence Laboratory at the University of Louisville and Duke Advanced Imaging Laboratory at Duke University Medical Center.

3 Background

3.1 Feature-based and featureless CAD systems The study was conducted in the context of the CAD systems for breast cancer detection. Such systems utilize the information extracted from mammograms (X-ray images of breast). Two main approaches were utilized: feature-based and featureless. In the feature-based approach, a set of features is extracted from an x-ray image using an image processing algorithm. Then, the features are utilized in the process of classification of the case. Within the classifiers that can be used in this paradigm are neural networks (NN), linear discriminant analysis (LDA), Bayesian classifier, case-based classifier and others. In the featureless approach, proposed by Dr. Tourassi [17], [18], a classical information-theoretic concept of mutual information is used in order to evaluate similarity between two images. Then, case-based principles are used to classify new, incoming cases based on the similarity between the query case and cases stored in the database of the system.

4 Methods

The majority of the methods and experiments are presented in the conference article which is the result of the research conducted within this project [19]. This paper is currently in press for Proceedings of SPIE Medical Imaging 2008. Therefore, only a brief summary of the methods and results are presented here. In this paper, we propose to use an ensemble technique to improve performance of previously presented information-theoretic CAD system (IT-CAD). In order to do so, the database of examples is divided into several exclusive subsets. The division is done randomly and using k-means clustering based on 7 textural features extracted from the ROIs. Then, linear discriminant analysis (LDA) is applied to combine the outputs of classiﬁers built based on these subsets. It has been demonstrated in the paper that this approach provides statistically signiﬁcant improvement in the performance measured using the receiver operator characteristic (ROC). In this document, two additional experiments are presented demonstrating an application of two

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Figure 1: Performance of the system from Experiment 1.

computational intelligence techniques, artiﬁcial neural networks and fuzzy clustering, to the ensemble framework.

5 Experiments

5.1 Database For the experimental purposes mammograms extracted from Digital Database for Screening Mammog- raphy (DDSM) [20] (University of Florida) were used. The mammograms were digitized using a LU- MISYS scanner. Regions of interest (ROIs) of 512 × 512 pixels were extracted from the mammograms. The resulting dataset consisted of 901 ROIs depicting masses and 919 ROIs depicting normal cases. The ROIs depicting normal cases were extracted from the normal mammograms in a random way.

5.2 Experiment 1: Incorporating a neural network to the ensemble system In the ﬁrst experiment, feedforward neural networks (instead of LDA) were applied to combine the ensemble elements. The set of training examples was constructed using leave-one-out approach, as shown in [19]. Three hidden neurons and one output neuron were used. The activation function for all the neurons was linear. Error backpropagation with momentum was used in the training. Here, I concentrate on k-means approach to the database decomposition as it turned out to be superior over random division. To assess the performance of k-means + NN approach, 10-fold crossvalidation

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Figure 2: Performance of the system from Experiment 2.

data handling was used to train and test the ensemble. I varied the number of elements of the ensemble between 2 and 20 with a step of 2. Figure 5.1 presents the performance of the system measured by the area under the ROC curve (AUC) for different number of elements of the ensemble. A large improvement of the performance can be clearly seen for all of the examined scenarios.

5.3 Experiment 2: Incorporating fuzzy clustering to the ensemble system In this experiment I went a step further and introduced c-means fuzzy clustering instead of previously used k-means clustering. Similarly as in Experiment 1, NN was used to combine the responses of the ensemble elements into one ﬁnal decision. Ten-fold crossvalidation was used to estimate the performance of the system. The results are shown in Fig. 2. It can be seen that the performance of the c-means-based system is comparable to the performance of the system using k-means. The only two observed difference are that the system based on fuzzy c-means had noticeably lower performance than the system based on k-means when only two clusters are used but on the other hand it provided more stable performance for higher number of clusters.

6 Conclusions

In this document, I presented the outcomes from the project sponsored by IEEE Computational Intel- ligence Society. The project involved cooperation between the Computational Intelligence Laboratory

4 at the University of Louisville and Duke Advanced Imaging Laboratories at Duke University Medical Center and it involved my visit at the later lab. The results of the research performed in this project were included in the conference article [19] and presented by me at the SPIE Medical Imaging 2008 conference in February 2008. Additional results were presented in this document.

7 Acknowledgments

I would like to acknowledge the two professors advising me in this project who co-authored the resulting article: Dr. Jacek M. Zurada and Dr. Georgia D. Tourassi, as well as Dr. Vasilis Megaloikonomou for his help in this project. I would also like to thank the members of the Computational Intelligence Laboratory at the University of Louisville and Duke Advanced Imaging Laboratories at Duke University Medical Center for their support.

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