Using an Adaptive Neuro-Fuzzy Inference System (AnFis) Algorithm for Automatic Diagnosis of Skin Cancer

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1 Journal of Communcaton and Computer 8 (2011) Usng an Adaptve Neuro-Fuzzy Inference System (AnFs) Algorthm for Automatc Dagnoss of Skn Cancer Suhal M. Odeh Department of Computer Informaton Systems, Bethlehem Unversty, Bethlehem 92248, Palestne Receved: May 10, 2010 / Accepted: May 28, 2010 / Publshed: September 30, Abstract: Ths paper presents a dagnoss system, based on an adaptve neuro-fuzzy nference system (ANFIS) algorthm, for applcatons n bomedcal felds. Ths paper deals specfcally wth skn cancer dagnoss. Our system can be dvded nto two man parts: feature selecton, usng the Greedy feature flp algorthm (G-flp), and classfcaton method usng ANFIS algorthm. The ANFIS algorthm could be traned wth the back propagaton gradent descent method n combnaton wth the least squares method. Three dfferent types of skn lesons were ntroduced to ths dagnoss system and the performance of the ANFIS model was evaluated n terms of tranng performance and classfcaton accuraces. The results confrmed that the proposed ANFIS model has potental n classfyng the skn cancer dagnoss. ey words: ANFIS, skn cancer, feature selecton, greedy feature flp algorthm. 1. Introducton The objectve of ths paper s to ntroduce ANFIS (Adaptve-Network-Based Fuzzy Inference Systems) as a dagnoss system. ANFIS has proven to be an excellent functon approxmaton tool, where t mplements a frst order Sugeno-style fuzzy system. The basc dea behnd these neuro-adaptve learnng technques s very smple. These technques provde a method for the fuzzy modellng procedure to acqure nformaton about a data set, n order to compute the membershp functon parameters that best allow the assocated fuzzy nference system track the gven nput/output data. Researchers that worked wth the same database by usng Prncpal Component analyss (PCA) and Independent Component Analyss (ICA) [1], but ther results on the dscrmnaton between Actnts eratoss and Basal Cell Carcnoma were very poor, and therefore ther conclusons about the fluorescence Correspondng author: Suhal M. Odeh, asstant professor, research felds: artfcal ntellgence, machne learnng, pattern recongnton, neural network. E-mal: sodeh@bethlehem.edu. technque for dscrmnatng these pathologes were very pessmstc [2]. The data nputs are features selected by the G-flp algorthm; from a number of features that were extracted from mages of three dfferent skn lesons (Fg. 1), are descrbed n the next secton. These mages have been obtaned by usng the fluorescence technque from the Insttute of Bophyscs (Unversty of Regensburg, Germany). These lesons can be classfed nto three groups: (1) Actnc eratoss or malgnant melanoma, a type of skn cancer known also as a solar keratoss, can be consdered as the frst step of the development of skn cancer); (2) Basal Cell Carcnoma s a cancer that begns n the deepest basal cell layer of the epderms (the outer layer of the skn); and (3) Psorass s a chronc skn condton whch tends to run n famles [3]. To avod the presence of the large numbers of features we used the feature selecton wth the task of selectng a small subset of these features, suffcent to predct the target class well. Accordngly, a small good

2 752 Usng an Adaptve Neuro-Fuzzy Inference System (AnFs) Algorthm for (a) Actnc eratoss (b) Basal Cell Carcnoma (c) Psorass Fg. 1 Samples of fluorescence mages of (a) Actnc eratoss (b) Basal Cell Carcnoma, and (c) Psorass. set of features could acheve a hgh performance level of classfcaton even wth usng the most basc classfers. Therefore feature selecton s crucal for effcent learnng. The mages shown weree taken from dfferent leson groups and classes appear smlar; they were transformed va several transfer functons whch are consdered as standard methods n mage processng. The feature extracton nvolves frst dentfyng features that dscrmnate among classes. In ths study a comprehensve lst of the features has been extracted and captured from dfferent mage treatment technques n whch specfc characterstcs of the mages have been ntroduced. 2. Feature Extracton Feature extracton nvolves smplfyng the amount of resources requred to descrbe a large set of data accurately. The features that were extracted n total 75 characterstcs or parameters. In order to avod hghly redundant features we have extracted the correlaton coeffcent matrx and dscarded those features wth a correlaton coeffcent above 0.98 wth respect to other features. In ths way only 39 out of the ntal 75 features have been selected. These features are extracted by usng dfferent algorthm of mage processng: (1) Edge detecton, wth two man categores methods, Gradent and Laplacan. The Gradent method, manly represented by three types (Sobel, Prewtt, and Canny), Refs. [4-6] detects the edges by lookng for the maxmum and mnmum values that satsfy the frst dervatve of the mage. The Laplacan method searches for zero crossngs n the second dervatve of the mage to fnd edges; (2) Fourerr transform; and (3) morphologc operatons, where the dlaton and eroson are consderedd as two fundamental morphologcal operatons; more detals about these feature can be found n Refs. [3, 7, 8]. These parameters have been extracted from database that contans 50 (pre-cancer), 50 mages of Basal Cell Carcnoma (cancer) and 67 mages of psorases. 3. Feature Selecton There s very large number of features, but only few of them are relevant for predctng the label. The featuree selecton s the task of choosng a small set out propertes of the data. The need for feature selecton arses to avod the presence of large number of weakly relevant and redundant features n the data set [9]. A good choce of features s a key for buldng compact and accurate classfers. mages of Actntc eratoss of a gven set of features that capture the relevant In ths paper the Greedy feature flp (G-flp) [10] algorthm s used to select a small set of features, whch can be used as nputs to our dagnoss system. G-flp s a greedy search algorthm for maxmzng the evaluaton functon e(f), where F s a set of features. The algorthm repeatedly terates over the feature set and updates the set of chosen features. In each teraton t s decded to remove or add the current feature to the selected set by evaluatng the margn term n the Eq. (1) wth and wthout ths feature. The followng equaton shows the evaluatng functon for a tranng set S and a weght vector w accordng to the second defnton of

3 753 the margn n Ref. [10] w e ( w) = Θ / ( x) (1) x s s x Ths algorthm converges to a local maxmum of the evaluaton functon, as each step ncreases ts value and the number of possble feature sets s fnte. The computatonal complexty of one pass over all features of Gflp s ( N 2 m 2 ) Θ (2) where N s the number of features and m s the number of nstances [10]. Emprcally G-flp converges after a few teratons and there s no need to tune the number of features or any type of threshold. 4. Classfcaton Scheme A modular classfcaton algorthm based on the ANFIS has been used for ths applcaton. ANFIS apples two technques n the updatng features. For premse features that defne membershp functons, ANFIS employs gradent descent for fne-tunng purposes. For consequent parameters that defne the coeffcents of each output equatons, ANFIS uses the least-squares method. Ths approach s called hybrd learnng method snce t combnes gradent descent and the least-squares methods [11]. In a fuzzy nference system, there are three types of nput space parttonng: grd, tree, and scatterng parttonng. The "curse of dmensonalty" refers to a stuaton where the number of fuzzy rules ncreases exponentally wth the number of nput varables [12, 13]. Therefore, sx features were used n the dagnoss system. These features have to be very accurate, so the features selecton algorthm, Greedy feature flp (G-flp) algorthm was used to determne our best 6 features out of the dataset of features that were extracted prevously. 4.1 ANFIS Structure In ths paper, for the classfcaton method the ANFIS algorthm was used n order to classfy the tral mages nto mages that belong to one of the three dfferent types of skn lesons mentoned earler. So ANFIS s network organzes two parts lke fuzzy systems. The frst part s the antecedent part and the second part s the concluson part that s connected to each other by rules, n network form. If ANFIS n network structure s shown, that s demonstrated n fve layers, t can be descrbed as a mult-layered neural network as shown n Fg. 2 [14]. Where the frst layer executes a fuzzfcaton process, the second layer executes the fuzzy AND of the antecedent part of the fuzzy rules, the thrd layer normalzes the membershp functons (MFs), the fourth layer executes the consequent part of the fuzzy rules, and fnally the last layer computes the output of fuzzy system by summng up the outputs of fourth layer. Here for ANFIS structure (Fg. 2) two nputs and two labels for each nput are consdered. The feed forward equatons of ANFIS are as follows: [11, 14-16] w = μ x μ y, =1, (3) Where ( ) ( ) 2 A B = w w, = 1,2 w + w (4) 1 2 w f + w f f = + (5) = w1 f1 w f 2 2 w1 + w2 f1 p1x + q1 y + r1 z f2 = p2x + q2 y + r2 = and z In order to model complex nonlnear systems, the ANFIS model carres out nput space parttonng that splts the nput space nto many local regons from whch smple local models (lnear functons or even adjustable coeffcents) are employed. The ANFIS uses fuzzy MFs for splttng each nput dmenson. The nput space s covered by overlappng MFs, whch means that several local regons can be actvated smultaneously by a sngle nput. As smple local models are adopted n ANFIS model, the ANFIS approxmaton ablty wll depend on the resoluton of the nput space parttonng, whch s determned by the number of MFs n ANFIS and the number of layers. Usually MFs are used as bell-shaped wth maxmum equal to 1 and mnmum equal to 0 such as [11, 14, 16]:

4 754 Fg. 2 The equvalent ANFIS (type-3 ANFIS). 1 μ A ( x) = b (6) 2 x c 1+ a 2 b x c μ ( ) A x = exp (7) a Where {a,b, c } are the parameters of MFs whch are affected n shape of MFs. The ANFIS uses member functon for each nput. The tranng was run for 10 teratons. The network performance was evaluated on the checkng set, after each teraton, by calculatng the root-mean-square errors (RMSE) ( Y Yˆ ) = 1 RMSE = (8) Where the k s the pattern number, k=1, 2..., Y s the correct value, and Ŷ k s the output value from the ANFIS. The RMSE was also calculated on tranng data set for every teraton. The optmal number of teratons obtaned was 9 epochs by the tme RMSE reached ts mnmum value. We then convert the error from RMSE to percentage error. 5. Expermental Results Before applyng ths methodology of the classfcaton, the posed dagnoss problem s dvded nto two tasks: (1) Easy Task: the dstncton between cancerous or pre-cancerous mages and psorass cases. Ths means, two classes: class 1 (cancer and pre - cancer) conssts of 33 mages of Actnc eratoss and 34 mages of Basal Cell Carcnoma, and class 2 (psorass) conssts of 67 mages; (2) Dffcult Task: the detecton of the cancer (Basal Cell Carcnoma) wthn the database s composed from 50 mages of Actnc eratoss cases and 50 mages of Basal Cell Carcnoma. Ths means, two classes: class 1 (cancer) and class 2 (pre-cancer). The tranng data set was made up from 80% of the overall data and the other 20% of the data s consdered as testng data set. Three versons of these data sets are used where each verson was randomly dsordered n order to cross valdate the results. After applyng the methodology and runnng the classfcaton algorthm for 10 teratons, t reached the mnmum RMSE value at the nnth epoch. The classfcaton accuracy n both of the easy task and the dffcult task, for each of these three versons are shown n Table 1. The ANFIS structure nformaton s shown n Table 2. From results tabled above, t can be seen that the usng of ANFIS classfcaton algorthms as a dagnoss system for bomedcal problems, have a hgh level of effcency performance, where t can accurately predct the testng n the easy task by approxmately 100%, and n the dffcult task by 92%, whch means that the ANFIS s powerful enough to be used as a dagnoss Table 1 The classfcaton accuracy of the easy and dffcult task. Easy task Dffcult task Verson Verson Verson The average accuracy ± ± 1.7 Table 2 The ANFIS structure nformaton. Number of nodes 1503 Number of lnear parameters 5103 Number of nonlnear parameters 54 Total number of parameters 5157 Number of tranng data pars 80 Number of checkng data pars 20 Number of fuzzy rules 729

5 755 system. Ths work was mplemented by usng MATLAB 7.5 under Wndows Vsta wth Intel Centrno processor runnng at 1.87 GHz. The tme spent to get the result of ths classfcaton was about 120 mnutes. The ANFIS classfcaton algorthms NN classfer optmzed wth GA represents a vald tool to study the sgnfcance of dfferent features for a gven dagnoss problem [3, 4]. We have tested other classfers such as NN classfer optmzed wth GA, and artfcal neural networks (ANN) (Multlayer Perceptron) yeldng lower level of accuraces and requrng much longer computng tmes. The low performance level of the ANN classfers s manly due to the lmted number of samples (167 mages) of the database. 6. Conclusons Ths paper ntroduces the adaptve neuro-fuzzy nference system (ANFIS) as a dagnoss system for bomedcal problems; the dagnoss of skn lesons. Ths system showed good performance accuracy, especally when compared wth other systems that use the same database of fluorescence mages. It can be concluded that the dagnoss systems based on ANFIS, can also be appled to other bomedcal feld applcatons, lke breast cancer, EEG sgnals, ECG sgnals, etc. Consequently, ths paper valdates the optmzaton technque of the dfferent features to a hgh level of classfcaton accuracy, where these features were extracted by mage processng and then selected by usng G-flp algorthm. Other publshed classfcaton methods, such as - nearest neghbor [1, 3, 4] wth genetc algorthms [17] whch uses the same data set as our study, have a lower level of accuracy results. The result of ths classfcaton method showed that by usng ANFIS, produces better result than wth other algorthms for dagnoss systems, n the feld of bomedcal studes. References [1] Ch. Mes, Ch. Bauer, G. Ackermann, W. Bäumler, C. Ables, C.C. Puntonet, M.R. Alvarez, E.W. Lang, Can ICA help classfy skn cancer and bengn lesons, LNCS 2001 (2085) [2] J.D. Carey, Skn cancer, Health Smart Quarterly Health Newsletter, The Unversty of New Mexco 12 (2003) 1-3. [3] E. Ros, M.M. Rodríguez, S. Mota, J.L. Berner, I. Rojas, C.G. Puntonet, E.W. Lang, Skn lesons dagnoss based on fluorescence mage processng, smple parameters scannng, LNCS 2562 (2003) [4] S. Odeh, E. Ros, I. Rojas, J. Palomares, Skn leson dagnoss usng fluorescence mages, Proceedng of ICIAR06, Internatonal Conference on Image Analyss and Recognton, 2006, pp [5] J. Canny, A computatonal approach to edge detecton, IEEE Transactons on Pattern Analyss and Machne Intellgence, PAMI, 1986, pp [6] J.R. Parker, Algorthms for Image Processng and Computer Vson, John Wley & Sons, 1997, pp [7] J.S. Lm, Two-Dmensonal Sgnal and Image Processng, Englewood Clffs, NJ, Prentce Hall, 1990, pp [8] R. Gonzalez, R. Woods, Dgtal Image Processng, Addson Wesley, 1992, pp [9] P.M. Narendra,. Fukunaga, A branch and bound algorthm for feature subset selecton, IEEE Transactons on Computers 26 (1977) [10] R.G. Bachrach, A. Navot, N. Tshby, Margn based feature selecton: theory and algorthms, ICML, [11] S. Odeh, J. Hodal, M. Sleb, I. Salsa, Cursor movement control development by usng ANFIS algorthm, The Internatonal Arab Journal of Informaton Technology (2009) [12] E. Ikonen,. Najm, Fuzzy neural networks and applcaton to the FBC process, Proceedng of IEEE - Control Theory Applcaton, 1996, pp [13] R.E. Bellman, Adaptve Control Processes, Prnceton Unv. Press, [14] F.A. Alturk, A.B. Abdennour, Neuro-fuzzy control of a steam boler turbne unt, Proceedng of the 1999 IEEE, Internatonal Conference on Control Applcatons, 1999, pp [15] S.H. m, Y.H. m,.b. Sm, H.T. Jeon, On developng an adaptve neural-fuzzy control system, Proceedng of IEEE/RSJ Conference on Intellgent Robots and Systems, 1993, pp [16] V.S. Ghomsheh, M.A. Shoorehdel, M. Teshnehlab, Tranng ANFIS structure wth modfed PSO algorthm, Proceedng of the 15th Mdetaeran on Control and Automaton, [17] S. Odeh, E. Ros, I. Rojas, A computer aded dagnoss systems: usng genetc algorthm wth classfer of the k-nearest neghbors, The Internatonal Arab Conference on Informaton Technology, Tuns, 2008.