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Fuzzy Application for Melanoma Cancer Risk Management

Joint Research : Bilqis Amaliah (ITS) and Rahmat Widyanto (UI). Fuzzy Application for Melanoma Cancer Risk Management. Contents. Introduction. Problem Formulation. Goal. Problem Restriction. Method. Testing. Result. Conclusion. S uggestion and Recommendation. Background.

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Fuzzy Application for Melanoma Cancer Risk Management

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  1. SocDic2011 Joint Research:BilqisAmaliah (ITS) and RahmatWidyanto (UI) Fuzzy Application for Melanoma Cancer Risk Management

  2. Contents Introduction Problem Formulation Goal Problem Restriction Method Testing Result Conclusion Suggestion and Recommendation SocDic2011

  3. Background Melanoma is one of skin cancer and deadly dangerous Early detection is necessary for the patient to get the right treatment Takagi - Sugeno Fuzzy Inference System (TS-FIS) has a simpler computing with better accuracy than existing methods (SVM, Boosting SVM, Voted Perceptron) SocDic2011

  4. Problem Formulation How to classify melanoma image using ABC feature and Takagi-Sugeno FIS ? Is Takagi-Sugeno FIS accuracy better than existing methods (SVM, Boosting SVM, Voted Perceptron) ? SocDic2011

  5. Goal Designing the image diagnosis system for determine whether melanoma or not SocDic2011

  6. Problem Restriction Image data must have a good resolution and not too small. The image is not covered by thick hair. There is only one object in the image. The system is built using MATLAB R2010. SocDic2011

  7. 5 Takagi-Sugeno FIS 4 Training Method 1 Preprocessing 2 Segmentation 3 Feature extraction Median Filtering Thresholding Asymmetry image intensity values Mapping Border Irregularity 6 Prediction Color Variation Flood - Filling SocDic2011

  8. Image 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 SocDic2011

  9. Feature Extraction • Asymmetry • Asymmetry Index (AI) • Lengthening Index(Å ) 1 2 3 • Border Irregularity • Compactness Index(CI) • Fractal Dimension(fd) • Edge Abruptness(Cr) • Pigmentation Transition (me, ve) 4 5 6 7 • Color Variation • Color homogeneity(Ch) • Correlation geometry • and photometry(Cpg) 8 9 SocDic2011

  10. TS FIS – Membership Function 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] F  M : [-108.3 -42.89 22.5] N : [-42.89 22.5 87.89] C M : [-58.15 0.8496 59.85] N : [0.8496 59.85 118.9] G  M : [0.02139 8.275e+004 1.655e+005] N : [-8.275e+004 0.02139 8.275e+004] D M : [-25.59 -13.27 -0.954] N : [-13.27 -0.954 11.36] H  M : [-253 0 253] N : [0 253 506] E M : [-0.1489 0.002281 0.1534] N : [0.002281 0.1534 0.3046] F  M : [-0.00125 9.6 19.2] N : [-9.602 -0.00125 9.6] SocDic2011

  11. TS FIS – Rule If (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 - SocDic2011

  12. Testing Feature Vector Dimension 200 DATA Trial Data Digit 1-2 : Asymmetry 100 Melanoma Image(+) Digit 3 - 7 : Border Irregularity 100 Non-Melanoma Image(-) Digit 8 - 9 : Color Variation SocDic2011

  13. Experiment Performance Testing (cont) Using 100 data of melanoma and 100 data of Non-Melanoma Performance is measured using Accuracy SocDic2011

  14. Testing (cont) ABC Feature Extraction Segmented Image Segmentation Output ofPreprocessing Preprocessing Input Image SocDic2011

  15. Testing (cont) Conclusion whether the imageis a melanoma or not Testing ofTakagi-Sugeno FIS If ( ) then (output) 1 2 3 4 5 6 7 8 9 Training ofTakagi-Sugeno FIS Training using 9 feature ABC Feature Extraction Segmented Image SocDic2011

  16. TS-FIS performance comparison with Voted Perceptron, SVM, and SVM boosting SocDic2011

  17. Accuracy 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. 2 image of melanoma can be classified based on ABC features, which is trained using Takagi-Sugeno Fuzzy Inference System 1 Conclusion SocDic2011

  18. Required the addition of trial data and feature selection on the development in order to improve performance. Improvement of segmentation by using another method Suggestion and Recommendation SocDic2011

  19. Thank you Kiitos Special Thank’s : H.Nobuhara R. Widyanto SocDic2011

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