LOGO Fuzzy Application for Melanoma Cancer Risk Management Joint Research: Bilqis Amaliah (ITS) and Rahmat Widyanto (UI) 1 SocDic2011

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

LOGO Melanoma is one of skin cancer and deadly dangerous Background 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) 3 SocDic2011

LOGO How 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) ? 4 SocDic2011

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

LOGO Image 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 R SocDic2011

LOGO Asymmetry Border Irregularity Color Variation 3Featureextraction3Featureextraction Median Filtering image intensity values Mapping Thresholding Flood - Filling Method 1Preprocessing1Preprocessing 2 Segmentation Prediction 6 4Training4Training 5Takagi-SugenoFIS5Takagi-SugenoFIS SocDic2011

LOGO 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 8 SocDic2011

LOGO Feature Extraction Asymmetry Asymmetry Index (AI) Lengthening Index ( Å ) Color Variation Color homogeneity (Ch) Correlation geometry and photometry (Cpg) Border Irregularity Compactness Index (CI) Fractal Dimension (fd) Edge Abruptness (Cr) Pigmentation Transition (me, ve) SocDic2011

LOGO TS FIS – Membership Function 10 A  M : [ ] N : [ ] B  M : [ ] N : [ ] C  M : [ ] N : [ ] D  M : [ ] N : [ ] E  M : [ ] N : [ ] F  M : [ ] N : [ ] G  M : [ e e+005] N : [-8.275e e+004] H  M : [ ] N : [ ] F  M : [ ] N : [ ] SocDic2011

LOGO 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 - Because there is no special weighting on 9 features, then : If (∆Melanoma) > (∆Non Melanoma) then output is Melanoma -And otherwise - SocDic2011

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

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

LOGO Testing (cont) ABC Feature Extraction Segmentation Preprocessing Output of Preprocessing Input Image 14 Segmented Image SocDic2011

LOGO Testing (cont) Conclusion whether the image is a melanoma or not Testing of Takagi-Sugeno FIS Training of Takagi-Sugeno FIS ABC Feature Extraction Training using 9 feature 15 Segmented Image If ( ) then (output) SocDic2011

LOGO TS-FIS performance comparison with Voted Perceptron, SVM, and SVM boosting 16 Classification Method Accuracy (%) Takagi-Sugeno FIS 82,5 Voted Perceptron77,5 SVM 74,4 SVMboosting75,2 SocDic2011

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

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

LOGO “ Add your company slogan ” 19SocDic2011 Special Thank’s :