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Rules for Melanoma Skin Cancer Diagnosis

Rules for Melanoma Skin Cancer Diagnosis W ł odzis ł aw Duch , K. Grąbczewski, R. Adamczak, K. Grudziński, Department of Computer Methods, Nicholas Copernicus University, Torun, Poland. http://www.phys.uni.torun.pl/kmk Zdzisław Hippe Department of Computer Chemistry and Physical Chemistry

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Rules for Melanoma Skin Cancer Diagnosis

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  1. Rules for Melanoma Skin Cancer Diagnosis Włodzisław Duch, K. Grąbczewski, R. Adamczak, K. Grudziński,Department of Computer Methods, Nicholas Copernicus University, Torun, Poland. http://www.phys.uni.torun.pl/kmk Zdzisław Hippe Department of Computer Chemistry and Physical Chemistry Rzeszów University of Technology, zshippe@prz.rzeszow.pl _____KOSYR 2001______

  2. Content: • Melanoma skin cancer data • 5 methods: GTS, SSV, MLP2LN, SSV, SBL, and their results. • Final comparison of results • Conclusions & future prospects _____KOSYR 2001______

  3. Skin cancer Most common skin cancer: • Basal cell carcinoma (rak podstawnokomórkowy) • Squamous cell carcinoma (rak kolczystonabłonkowy) • Melanoma: uncontrolled growth of melanocytes, the skin cells that produce the skin pigment melanin. • Too much exposure to the sun, sunburn. • Melanoma is 4% of skin cancers, most difficult to control, 1:79 Americans will develop melanoma. • Almost 2000 percent increase since 1930. • Survival now 84%, early detection 95%. _____KOSYR 2001______

  4. Melanoma skin cancer data summary • Collected in the Outpatient Center of Dermatology in Rzeszów, Poland. • Four types of Melanoma: benign, blue, suspicious, or malignant. • 250 cases, with almost equal class distribution. • Each record in the database has 13 attributes. • TDS (Total Dermatoscopy Score) - single index • 26 new test cases. • Goal: understand the data, find simple description. _____KOSYR 2001______

  5. Melanoma AB attributes • Asymmetry: symmetric-spot, 1-axial asymmetry, and 2-axial asymmetry. • Border irregularity: The edges are ragged, notched, or blurred.Integer, from 0 to 8. _____KOSYR 2001______

  6. Melanoma CD attributes • Color: white, blue, black, red, light brown, and dark brown; several colors are possible simultaneously. • Diversity: pigment globules, pigment dots, pigment network, branched strikes, structureless areas. _____KOSYR 2001______

  7. Melanoma TDS index • Combine ABCD attributes to form one index: • TDS index ABCD formula: TDS = 1.3 Asymmetry + 0.1 Border + 0.5 S {Colors} + 0.5 S {Diversities} Coefficients from statistical analysis. _____KOSYR 2001______

  8. Remarks on testing • Test: only 26 cases for 4 classes. • Estimation of expected statistical accuracy on 276 training + test cases with 10-fold crossvalidation.Not done with most methods! • Risk matrices desirable: identification of Blue nevus instead Benign nevus carries no risk, but with malignant great risk. _____KOSYR 2001______

  9. Methods used: GTS • GTS covering algorithm (Hippe, 1997) + recursive reduction of the number of decision rules. • Interactive, user guides the development of the learning model. • Selection of combination of attributes generating learning model is based on Frequency and Ranking. • GTS allows to create many different sets of rules. • In a complex situation may be rather difficult to use. _____KOSYR 2001______

  10. GTS results. • GTS generated a large number (198) of rules. • Experimentation allowed to find important attributes. • Various sets of decision rules were generated: TDS & C-blue & Asymmetry & Border (4 attributes, based on the experience of medical doctors)TDS & C-blue & D-structureless-areas (3 attributes) TDS & C-Blue (2 attributes)TDS (1 attribute) - poor results. Models with 2-4 attributes give 81-85% accuracy. • Combination and generalization of these rules allowed to select 4 simplified best rules. • Overall: 6 errors on training, 0 errors on test set. _____KOSYR 2001______

  11. Methods used: SSV • Decision tree (Grąbczewski, Duch 1999) • Based on a separability criterion: max. index of separability for a given split value for continuous attribute or a subset of discrete values. • Easily converted into a set of crisp logical rules. • Pruning used to ensure the simplest set of rules that generalize well. • Fully automatic, very efficient, crossvalidation tests provide estimation of statistical accuracy. _____KOSYR 2001______

  12. SSV results • Pruning degree is the only user-defined parameter. • Finds TDS, C-BLUE as most important. • Rules are easy to understand: IF TDS  4.85  C-BLUE is absent => Benign-nevusIF TDS  4.85  C-BLUE is present => Blue-nevusIF 4.85 < TDS < 5.45 => SuspiciousIF TDS  5.45 => Malignant • 98% accuracy on training, 100% test. • 5 errors, vector pairs from C1/C2 have identical TDS & C-BLUE. • 10xCV on all data: 97.5±0.3% _____KOSYR 2001______

  13. Methods used: MLP2LN • Constructive constrained MLP algorithm, 0, ±1 weights at the end of training. • MLP is converted into LN, network performing logical function (Duch, Adamczak, Grąbczewski 1996) • Network function is written as a set of crisp logical rules. • Automatic determination of crisp and fuzzy "soft-trapezoidal" membership functions. • Tradeoff: simplicity vs. accuracy explored. • Tradeoff: confidence vs. rejection rate explored. • Almost fully automatic algorithm. _____KOSYR 2001______

  14. MLP2LN results • Very similar rules as for the SSV found. • Confusion matrix: Original class Benign Blue- Malig- Suspi- Calculated nevus nevus nant cious Benign-nevus 62 5 0 0 Blue-nevus 0 59 0 0 Malignant 0 0 62 0 Suspicious 0 0 0 62 _____KOSYR 2001______

  15. Methods used: FSM • Feature-Space Mapping (Duch 1994) • FSM estimates probability density of training data. • Neuro-fuzzy system, based on separable transfer functions. • Constructive learning algorithm with feature selection and network pruning. • Each transfer function component is a context-dependent membership function. • Crisp logic rules from rectangular functions. • Trapezoidal, triangular, Gaussian f. for fuzzy logic rules. _____KOSYR 2001______

  16. FSM results • Rectangular functions used for C-rules. • 7 nodes (rules) created on average. • 10xCV accuracy on training 95.5±1.0%, test 100%. • Committee of 20 FSM networks: 95.5±1.1%, test 92.6%. • F-rules, Gaussian membership functions: 15 fuzzy rules, lower accuracy. • Simplest solution should strongly be preferred. _____KOSYR 2001______

  17. Methods used: SBL • Similarity-Based-Methods: many models based on evaluation of similarity. • Similarity-Based-Learner (SBL): software implementation of SBM. • Various extensions of the k-nearest neighbor algorithms. • S-rules, more general than C-rules and F-rules. • Small number of prototype cases used to explain the data class structure. _____KOSYR 2001______

  18. SBL results • SBL optimized performing 10xCV on training set. • Manhattan distance, feature selection: TDS & C_Blue • 97.4 ± 0.3% on training, 100% test. • S-rules of the form: IF (X sim Pi) THEN C(X)=C(Pi)IF (|TDS(X)-TDS(Pi)|+|C_blue(X)-C_blue (Pi)|)<T (Pi) THEN C(X)=C(Pi) Prototype selection left 13 vectors (7 for Benign-nevus class, 2 for every other class.97.5% or 6 errors on training (237 vectors), 100% test • 7 prototypes: 91.4% training (243 vectors), 100% test _____KOSYR 2001______

  19. Results - comparison Method Rules Training % Test% SSV Tree, crisp rules 4 97.5±0.3 100MLP2LN, crisp rules 4 98.0 all 100 GTS - final simplified 4 97.6 all 100 FSM, rectangular f. 7 95.5±1.0 100±0.0 knn+ prototype selection 13 97.5±0.0 100 FSM, Gaussian f. 15 93.7±1.0 95±3.6 GTS initial rules 198 85 all 84.6knn k=1, Manh, 2 feat. 250 97.4±0.3 100LERS, weighted rules 21 -- 96.2 _____KOSYR 2001______

  20. Conclusions: • TDS - most important; Color-blue second. • Without TDS - many rules. • Optimize TDS: automatic aggregation of features, ex. 2-layered neural network. • Very simple and reliable rules have been found. • S-rules are being improved - prototypes obtained from learning instead of selection. • Data base is expanding; need for non-cancer data. _____KOSYR 2001______

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