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Fuzzy Application for Melanoma Cancer Risk ManagementJoint Research:Bilqis Amaliah (ITS) and Rahmat Widyanto (UI)

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LOGO1ContentsTestingMethodGoalProblem FormulationIntroductionConclusionResultProblem RestrictionSuggestion and Recommendation

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LOGO2Melanoma is one of skin cancer and deadly dangerousBackgroundEarly detection is necessary for the patient to get the right treatmentTakagi - Sugeno Fuzzy Inference System (TS-FIS) has a simpler computing with better accuracy than existing methods (SVM, Boosting SVM, Voted Perceptron)

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LOGO3How to classify melanoma image using ABC feature and Takagi-Sugeno FIS ?Problem Formulation

Is Takagi-Sugeno FIS accuracy better than existing methods (SVM, Boosting SVM, Voted Perceptron) ?

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LOGO4 Designing the image diagnosis system for determine whether melanoma or notGoal

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LOGO5Image data must have a good resolution and not too small.Problem Restriction

The image is not covered by thick hair.

There is only one object in the image.

The system is built using MATLAB R2010.

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LOGO6AsymmetryBorder IrregularityColor Variation3FeatureextractionMedian Filteringimage intensity values Mapping ThresholdingFlood - FillingMethod1Preprocessing2Segmentation7

6Prediction4Training5Takagi-SugenoFISSocDic2011

LOGO7Image Processing

[1] Input Image

[2] Median Filter Image[3] Grayscale Image[4] Contrasted Image[8] Result Image[7] Filled Image[6] Inverted BW Image[5] Black and White Image

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789456Feature Extraction9123Asymmetry Asymmetry Index (AI)Lengthening Index ( )Color Variation Color homogeneity (Ch)Correlation geometry and photometry (Cpg)Border IrregularityCompactness Index (CI)Fractal Dimension (fd)Edge Abruptness (Cr)Pigmentation Transition (me, ve)

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LOGO9TS FIS Membership Function10

A M : [-0.2395 0.03768 0.3149] N : [0.03768 0.3149 0.592] B M : [-0.5161 0.2084 0.9329] N : [0.2084 0.9329 1.657] C M : [-58.15 0.8496 59.85] N : [0.8496 59.85 118.9] D M : [-25.59 -13.27 -0.954] N : [-13.27 -0.954 11.36] E M : [-0.1489 0.002281 0.1534] N : [0.002281 0.1534 0.3046] F M : [-108.3 -42.89 22.5] N : [-42.89 22.5 87.89] G M : [0.02139 8.275e+004 1.655e+005] N : [-8.275e+004 0.02139 8.275e+004] H M : [-253 0 253] N : [0 253 506] F M : [-0.00125 9.6 19.2] N : [-9.602 -0.00125 9.6]SocDic2011

LOGO-yang dicontohkan diatas adalah MF fitur A-Parameter batas (melanoma M dan non melanoma N) pada membership function fitur A sampai Z didapatkan melalui training data hasil ekstraksi fitur (pada proses sebelumnya)-data yang digunakan untuk training tersebut adalah data yang sudah BENAR-

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TS FIS RuleIf (A is (M/N) and (B is (M/N) and and (I is (M/N) then (output is (M/N) 512 rule (2^9)Because there is no special weighting on 9 features, then :

If (Melanoma) > (Non Melanoma) then output is Melanoma And otherwise -

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LOGO11TestingTrial Data200 DATA100 Melanoma Image(+)100 Non-Melanoma Image (-)Digit 8 - 9 : Color VariationFeature Vector DimensionDigit 1-2 : AsymmetryDigit 3 - 7 : Border Irregularity12SocDic201112Testing (cont)ExperimentPerformanceUsing 100 data of melanoma and 100 data of Non-MelanomaPerformance is measured using Accuracy13SocDic201113Testing (cont)ABC Feature ExtractionSegmentationPreprocessingOutput of PreprocessingInput Image

14Segmented Image

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LOGO14Testing (cont)Conclusion whether the imageis a melanoma or notTesting of Takagi-Sugeno FISTraining of Takagi-Sugeno FISABC Feature ExtractionTraining using 9 feature15

Segmented Image1 2 3 4 5 6 7 8 9

If ( ) then (output)SocDic2011

LOGO15TS-FIS performance comparison with Voted Perceptron, SVM, and SVM boosting16Classification MethodAccuracy (%)Takagi-Sugeno FIS82,5Voted Perceptron77,5SVM74,4SVMboosting75,2

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LOGO16Conclusion2Accuracy of TS-FIS is higher by 5% if compared to the Voted Perceptron, 8.1% higher when compared with SVM, and 7.3% higher when compared with SVMboosting.1image of melanoma can be classified based on ABC features, which is trained using Takagi-Sugeno Fuzzy Inference System17

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LOGO17Suggestion and RecommendationRequired the addition of trial data and feature selection on the development in order to improve performance.Improvement of segmentation by using another method18

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LOGO18Thank you19

KiitosSocDic2011H.NobuharaR. WidyantoSpecial Thanks :LOGO Add your company slogan

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