Ovarian Cancer Diagnosis UsingComplementary Learning Fuzzy Neural

NetworkT.Z. Tan, C. Quek, G.S. Ng

Centre for Computational Intelligence(formerly known as Intelligent System Lab)

School of Computer Engineering, Nanyang Technological UniversityBlk N4, #Bla-02, Nanyang Avenue, Singapore 639798

Abstract-DNA microarray is an emerging technique in ovariancancer diagnosis. However, very often, microarray data is ultra-huge and difficult to analyze. Thus, it is desirable to utilize FuzyNeural Network (FNN) approach for assisting the diagnosis andanalysis process. Amongst FNN, complementary learning FNN isable to rapidly derive fuzzy sets and formulate fuzzy rules.Complementary learning FNN uses positive and negativelearning, and hence it subsides the effect of curse of dimensionand is capable of modeling the dynamics of problem space withrelative good classification performance. Furthermore,FALCON-AART has human-like reasoning that allowsphysician to examine its computation in a familiar way.FALCON-AART can generate intuitive fuzzy rule to justify itsreasoning, which is important to generate trust among the usersof the system. Hence, FALCON-AART is applied in ovariancancer diagnosis as a clinical decision support system in thiswork. Its experimental results are encouraging.

Keywords: Backpropagation, FALCON-ART, fuizzy adaptivelearning control network fuzzy neural network, OvarianCancer.

I. Introduction

Fuzzy Neural Network (FNN) is a hybrid architecture thatadopts concepts from fuizzy logic and neural network. As aresult, it can model the problem space better and at the sametime provides human-like reasoning for its output. Hence,FNN has been applied in various areas as a decision supportsystem.

Complementary Learning FNN (CLFNN) such as FuzzyAdaptive Learning Control Network implementing AdaptiveResonance Theory (FALCON-ART) [1] is able to derive andformulate its fuizzy rule base automatically without priorknowledge of the problem domain. By using positive andnegative learning, CLFNN reflects the dynamic of theproblem space better and at the same time obtains relativelysuperior classification result. Besides, CLFNN autonomouslygenerates complementary rules for its reasoning process,

which is a highly favourable feature for Clinical DecisionSupport System (CDSS) because these complementary rulescan be used to justify its computation as well as serve asdiagnostic decision adjunct. Moreover, its reasoning processis highly akin to that of physicians, hence, allow physicians toexamine its reasoning process in their familiar term. Thus, inthis paper, a complementary learning FNN called FALCON-Another ART (FALCON-AART) [2] is proposed as a CDSSfor ovarian cancer diagnosis.

Ovarian cancer begins in the cells that constitute the ovaries,including surface epithelial cells, germ cells, and the sexcord-stromal cells [3][4]. Ovarian cancer accounts for fourpercent of all cancers among women and ranks fourth as acause of their deaths from cancer [5]. This high death rate isdue to the fact that almost 70% of women with the epithelialovarian cancer are not diagnosed until the disease is advancedto Stage III (i.e. cancer has spread to upper abdomen) orbeyond. Thus, early detection is paramount. Moreover,treatment option depends on the type and the stage of theovarian cancer has advanced [6][7]. Therefore, it is crucial todetect ovarian cancer early, and identify the ovarian cancerstaging accurately.

Apart from two common techniques physician adopted todiagnose ovarian cancer, namely sonography and blood test,DNA microarray is a novel alternative developed for thatpurpose [8]. For classic technique such as sonography, thediagnosis is based on analysing ultrasound data obtained frommanual examination of pelvic masses sonography images,consequently the accuracy of the diagnosis relies heavily onthe experiences and skill of the examiner. Owing to that, themost common method used is Cancer-Antigen 125 test (CA-125) [9], a blood test that determines the level of an antigen inthe blood, and is well known as tumor marker. It is commonlyused to monitor the state of the disease in ovarian cancerpatients, because 80-90% ofwomen with ovarian cancer in itslater stages will have signs of the antigen in their blood.

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Unfortunately, it is neither sensitive nor specific, and itsaccuracy is around 70%. Therefore, many research workshave been done on analysing DNA microarray for ovariancancer diagnosis [10][11][12][13]. However, analysing geneexpression can be time-consuming as the number of genes isextremely huge. Thus, FALCON-AART is applied to assist inovarian cancer diagnosis based on gene expression.

This paper is organized as follow. Section II describes thedataset used, the features of FALCON-AART, and theexperimental settings. Experimental result and analysis arepresented in Section III. Section IV concludes the paper.

II. Materials and Methods

2. 1. DataA DNA microarray gene expression dataset is used to assessFALCON-AART. The ovarian cancer dataset [14] consists of30 examples obtained from ovarian tumors and 24 normalexamples. Each of these examples comprises 1536 features.These datasets are available at http:Hl1ara.enm.bris.ac.uk/colin.Class distribution of this ovarian cancer dataset is as follows:

* 24 samples of normal ovarian tumor* 1 sample of Stage I mucinous ovarian tumor* 24 samples of Stage III serous ovarian tumor* 5 samples of Stage IV serous ovarian tumor

2.2. FALCON-AART

FALCON-AART is a modified version of FALCON-ART [1]and FALCON-Modified ART (FALCON-MART) [15]. Itgenerates fuizzy rules autonomously in the form described byEquation 1.

IF xi isAandx2 isB, THEN y1 isC and y2 isD (1)The fuzzy rule in Equation 1 is an example of a system withtwo inputs and two outputs. It consists of five elements:

* Input linguistic variables (x , x2 )* Input linguistic terms (A, B). This represents fuzzy

entities such as tall, short, thin, fat, and so on.FALCON-AART represents input linguistic termsby using trapezoidal membership function.

* If-Then rule: links the antecedent part (i.e. inputlinguistic variables and terms as above) with theconsequent part (i.e. output linguistic variables andterms as below).

* Output linguistic variables (YI I Y2)* Output linguistic terms (C, D).

FALCON-AART consists of five layers and each layer ismapped into the elements of the fuzzy rule (see Figure 1).

Before training commences, FALCON-AART has only inputand output layers. As training progresses, FALCON-AARTself-evolves and automatically constructs its hidden layer bymodified Fuzzy ART algorithm [2]. The modified fuzzy ARTalgorithm improves Fuzzy ART [16] by incorporating FullySelf-Organized ART (FOSART) [17] learning algorithm intothe Fuzzy ART algorithm. It dynamically partitions the inputand output spaces into trapezoidal fuzzy clusters, andsubsequently these clusters are fmetuned using adaptive back-propagation algorithm. If training patterns are similar enoughto the stored cluster, the stored cluster will resonate. Theresonant cluster will then expand to incorporate thesepatterns. Training terminates when the mean square errorsbetween two epochs are sufficiently equal.

Complementary learning refers to positive and negativelearning. The links between Layers 2 and 3 are the structuresresponsible for realizing this complementary learningparadigm, in which the knowledge derived from positive andnegative samples is separated. Therefore, when a positivesample is presented, only positive rules will be fired, wherenegative rules will be inhibited. This minimizes the confusionthat occurs in the inference process and may potentiallyimprove the recognition.

2.3. ExperimentExamination of the gene expression identified by DNAmicroarray provides important insight into the biology ofovarian cancer stages. However, gene expression obtained bymicrobiology method, DNA microarray, contains largenumber of features (1536 genes for this dataset). It isimpossible to employ all the features to classify. Even if itwas possible, there will be redundant genes that maysignificantly contribute to classification error. Owing to that,feature selection has to be done to select the most relevantfeatures before it is used in the classification task.

Sparse logistic regression [18] is used to select the nine mostrelevant features. Because it is difficult to choose the optimalfeatures (genes) that distinctively classify all the four classes,divide-and-conquer approach is adopted. 4-class classificationproblem is now reduced to four simpler 2-class classificationproblems. This classifier system consists of four FALCON-AART: each FALCON-AART is trained to handle one class.

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Layer 5(Output linguistic nodes)

t Feed-forward operation

Structural Learning

Layer 4(Outputlinguistic terns)

Layer 3(Rule nodes)

ILayer 2(Inputlinguistic terms)

,VLM)

Layer 1 ( Ia

.(Input linguistic nodes)

Xl xi I

Figure 1: Architecture of FALCON-AART. Each layer represents an element in the if-then fuzzy rule. Layer 1 is the input andLayer 2 is the linguistic term that symbolizes the antecedent/premise of the fuzzy rule. Layer 3 is the rule layer, and each noderepresents one rule. Layer 4 is the output linguistic term, forming the consequent/condition of the fuizzy rule. Layer 5 is theoutput. Layers 2-4 are self-organized layers. During training phase, input is fed in from Layer 1, and desired output is fed infrom Layer 5 simultaneously. During inference phase, the network operates in feed-forward manner.

For example, if FALCON-AART is used as Stage II ovariancancer classifier, then Stage II (positive) samples and non-Stage II (negative) samples will be used to train the system.Thus, whenever Stage II samples are presented to thenetwork, positive (Stage II) rules will have better matchingthan negative (non-Stage II) rules. As a result, positive rulesare activated and at the same time negative rules are inhibited,leading to a positive decision.

Each FALCON-AART is trained with different set of trainingand testing data (1/3 of data for training, 2/3 of data fortesting). Each class is trained with three random sets oftraining/testing and then the classification result is averaged.

III. Results

Classification performance of FALCON-AART isbenchmarked against Multilayer Perceptron (MLP) [19],Linear Discriminant Analysis (LDA) [20], its ancestorsFALCON-ART and FALCON-MART. The results arepresented in Table 2. Sensitivity and specificity are used asbenchmarking measure and are defined in Equations 2 and 3,respectively.

Sensitivity = Number of positive samples correctly predicted (2)Total number of positive samples

Specificity = Number of negative samples correctly predicted (3)Total number of negative samples

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The setting for 3-layer MLP is nine input nodes, eight hiddennodes, and an output node. Both hidden nodes and outputnode activation function are sigmoid function. It is trainedwith backpropagation with momentum until the mean squareerror of training set reaches 10-8.

From Table 1, mean classification rate of FALCON-AARTfor ovarian cancer diagnosis is 84.72%. Since the number ofsamples available for ovarian cancer diagnosis is limited, theoverall accuracy will drop drastically even if only one sampleis misclassified. However, FALCON-AART still outperformsMLP and LDA for every classification task, as well as itsancestors in most of the classification task. Besides,FALCON-AART has significantly reduced the training timerequired. Note that the network itself determines the number

of epochs required. Testing time is negligible for all themethods presented in this study.

The most crucial advantage of FALCON-AART is its abilityto provide reasons for its computation. In contrast, MLP,LDA act as black box to user, their output cannot bequantitatively evaluated and interpreted in an intuitive andhuman-like manner. FALCON-AART generates a relativelymore compact and precise rule base compared to its ancestors,which is highly desirable as this improves the interpretabilityof the system. Table 2 lists down two complementary rulesgenerated autonomously by FALCON-AART. The linguistictenns such as very low, medium, high, are characterised byfuzzy sets that are constructed dynamically. Examples offuizzy sets constructed are shown in Figure 2.

Table 1: Performance on ovarian cancer diagnosisMethod Class Sensitivity Specificity Accuracy Training Number of

(%/0) (%/) (°/.) Time Rules(Epoch)

FALCON- Normal 75.0 80.0 77.78 7 5AART Stage 1 100.0 100.0 100.0 5 13

Stage 3 50.0 85.0 69.44 2 5Stage 4 60.0 96.77 91.67 3 9Mean 84.72 4 8

MLP Nonnal 56.25 50.0 53.13 10000 NotStage I 100.0 88.57 94.29 10000 ApplicableStage 3 43.75 60.0 51.88 10000Stage 4 60.0 84.38 72.19 10000Mean 67.87 10000

Linear Normal 62.5 80.0 72.22 1 NotDiscriminant Stage I 100.0 58.34 59.46 1 ApplicableAnalysis Stage 3 37.5 45.0 41.67 I

Stage 4 100.0 41.94 50.0 1Mean 55.84 1

FALCON-ART Normal 50.0 80.0 66.67 100 90Stage 1 100.0 100.0 100.0 100 87Stage 3 62.5 55.0 58.33 100 90Stage 4 100.0 96.77 97.22 100 98Mean 80.56 100 91

FALCON- Normal 50.0 80.0 66.67 6 17MART Stage I 100.0 91.67 91.89 6 18

Stage 3 56.25 55.0 55.56 6 18Stage 4 100.0 51.61 58.33 10 32Mean 68.11 7 21

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Table 2: Complementary rules generated by FALCON-AARTPositive IF gene I value is very low AND gene2 value isRule very low AND gene3 value is medium AND

gene4 is medium AND geneS value is high ANDgene6 value is marginal low AND gene7 value isextremely low AND gene8 value is low ANDgene9 value is very low THEN Stage IV OvarianCancer

Negative IF gene 1 value is marginal low AND gene2 valueRule is medium AND gene3 value is marginal high

AND gene4 value is marginal high AND gene5value is marginal high AND gene6 value isextremely high AND gene7 value is low ANDgene8 value is high AND gene9 value is lowTHEN not Stage IV Ovarian Cancer

~0.8 I> ~~ ~ ~~--Rulel0.6 Rule2

.~0.4 ''-(-Rule3Eo 02

00 2

Gene Value

Figure 2: Fuzzy set constructed

Table 3: Diagnostic decision making processSteps Physician FALCON-AART

Observation and information Receiving datacollection (symptoms, patient sample.physiological condition, etc)

2 Based on the observation, Compute firinggenerate a set of hypotheses and strength of each ruleevaluate these hypotheses

3 Select the closest and best- Select the rule withmatching hypothesis maximum firing

strength4 Generate conclusion Derive conclusion

based on consequentfizzy sets

5 Gives the decision Output conclusion

From Table 3, one can see that inference process ofFALCON-AART is closely similar to human reasoningprocess: (1) Physician observes (FALCON-AART receivesdata sample), (2) Physician determines the matching degreeor closeness of the current observation with his/herknowledge and experiences (FALCON-AART calculates thematching degree of each rule), (3) Physician selects the best-matched experience or knowledge (FALCON-AARTactivates the rule with maximum overall matching degree),(4) & (5) Physician evaluates the selected knowledge and

gives his/her conclusion (FALCON-AART determines theconsequent linked to the activated rule and derivingconclusion).

IV. Conclusion

FALCON-AART has exhibited itself as a promising CDSSfor ovarian cancer diagnosis. Fast training, simple fuzzy rulegeneration, and superior accuracy are demonstrated byFALCON-AART in this preliminary study. One paramountfeature FALCON-AART offers is its ability to generatecomplementary fuzzy rules for its reasoning process. Theserules can aid physicians in their analysis as well as in makingtheir diagnostic decision. As the experiment result shows,FALCON-AART gives superior accuracy than otherconventional approaches in ovarian diagnosis. Hence, notonly FALCON-AART can potentially reduce the possibilitiesof medication errors, it also avoids the time-consumingprocess of deriving knowledge from large dataset such asmicroarray by hand. It outperforms MLP, LDA, and itsancestors in terms of accuracy, interpretability, and trainingtime. In future work, FALCON-AART-based CDSS can bebuilt based on other clinical ovarian cancer diagnosis methodsuch as sonography and blood test as well. These decisionsupport systems can then complement each other in ovariancancer diagnosis, and this is believed to enhance thediagnostic accuracy.

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