1. A Comparison Among Different Intelligent Techniques for the Automated Identification of Cancerous Smears G. Panagi 1, B. Bjerregaard 2, J. Jantzen 3, N. Ampazis 4, A. Tsakonas 5, E. Panourgias 6 and G. Dounias 4 1 Department of Radiology, General Hospital of Chios “Skilitsion”, Chios, Greece 2 Herlev University Hospital, DK-2730 Herlev, Denmark 3 Oersted-DTU Automation, Technical Univ. of Denmark,, Dk-2800 Kongens Lyngby, Denmark 4 Dept. of Financial & Management Engineering, University of the Aegean, 31 Fostini Str., 82100 Chios, Greece, g.dounias@aegean.gr 5 Artificial Intelligence and Information Analysis Lab, Dept. of Informatics, Aristotle University of Thessaloniki, Box 451 Thessaloniki GR-54124 tsakonas@zeus.csd.auth.gr 6 Department of Radiology, Euroclinic Hospital, 9 Athanasiadou Str., 11521 Athens, Greece

2. Cervical carcinoma Cervical carcinoma is the most common malignancy of the genital system of women Cancerogenesis: Normal epithelium Precancerosis (not invasive) Cancer Dysplasias 20-35 years Carcinoma in situ 25-40 years Cancer 45-55 years

3. FIGO classification of cervical cancer definitiontherapy5-year-healing rates Stadium 0 Carcinoma in situConisation 99% Stadium I restricted to cervixHysterectomy 75-90% Stadium II Infiltration of vulva or parametrium Radiation therapy (and operation) 50-70% Stadium III Infiltration of >2/3 of vagina or parametrium (pelvis) Radiation therapy only 30% Stadium IV Metastasis or infiltration of other organs Palliative therapy only 0-5%

4. Gross Anatomy Uterus Cervix Vagina

5. Histology vaginal parts of the cervix endocervical part of the cervix J = junction SS = stratified squamous epithelium SC = simple columnar epithelium 

6. Schematic drawing of the uterus and the cervix. The drawing also shows the transformation zone where the extracervical squamous epithelium meets the endocervical columnar epithelium. (Source: Byriel, 1999)

7. Basal lamina Stratum basale Stratum spinosum Stratum superficiale Stratified squamous epithelium DysplasiaCarcinoma in situ Connective tissue 

8. Papanicolaou classification CytologyWhat to do I Normal cells II Inflammatory, regenerative, metaplastic or degenerative cells III Very inflammatory or degenerative cells, dysplasia Papsmear control in short time (3 month) IV Very dysplastic cells or carcinoma in situ biopsy V Cells of invasive cervix carcinomabiopsy Material not suitableNew Papsmear

9. Pap smear cells AIS CIS Cylinder epithel Parabasel Metaplastics The smear colouring machine Pladeepithelet Cylinderepithel Hyperkromatiske kerner Multiple og uregel. nukleoler Baggrund med tumordiatese Indbyrdes stor variation I cellestørelse og form

10. Examples Pap I: normal cells 1=superficial cells 2=intermediate cells Pap II: inflammatory cells with Trichomonas (3) 

11. Pap III (D): dyskaryosis (different shapes of cell nuclei) Pap IV (a): dyskaryosis of deeper cell laminas with severe dysplasia 

12. Pap IV (b): uniform atypical cells, carcinoma in situ Pap V: polymorphic atypical cells, invasive carcinoma 

13. Aim of the Study The pap-smear classification problem through several C.I. Techniques Discussion on the obtained results Current Status: Manual, time-consuming technique Prospect: Automated, computer assisted smear classification and diagnosis

14. Materials  500 cases  20 numerical attributes representing typical cell measurements Nucleus area, cytoplasm area nucleus and cytoplasm brightness Nucleus and cytoplasm shortest and longest diameter Nucleus and cytoplasm perimeter etc Byriel J., 1999, "Neuro-Fuzzy Classification of Cells in Cervical Smears", MSc Thesis, Technical University of Denmark, Dept. of Automation, http://fuzzy.iau.dtu.dk/download/byriel99.pdf DATABASE: http://fuzzy.iau.dtu.dk/Byriel.nsf/AllSmears

15. Materials Analysis

16. Methods CLASSIFICATION C.I. METHODS APPLIED:   G-K (Gustafson-Kessel) Clustering Method   Feature Selection & G-K Clustering Method   FCM (Fuzzy C-Means)  HCM (Hard C-Means)   Inductive Machine Learning (C4.5 Standard & Boost)  Neuro-Fuzzy Classification (ANFIS Method)  Nearest Neighbour Classification (NNH-Method)  Standard GP (Genetic Programming)  PST-GP: GP-derived Crisp Rule-Based System  LMAM and OLMAM (2 nd Order Neural Network) Techniques RELATED PUBLICATIONS  Byriel J., 1999, "Neuro-Fuzzy Classification of Cells in Cervical Smears", MSc Thesis, Technical University of Denmark, Dept. of Automation, http://fuzzy.iau.dtu.dk/download/byriel99.pdf  Martin Erik, 2003, Pap-Smear Classification, MSc Thesis, Technical University of Denmark, Dept. of Automation. J. Jantzen: Neurofuzzy Modelling. Technical University of Denmark: Oersted-DTU, Tech report no 98-H-874 (nfmod), 1998. URL http://fuzzy.iau.dtu.dk/download/nfmod.pdf PapSmear tutorial. URL http://fuzzy.iau.dtu.dk/smear/ Tsakonas A., Dounias G., Jantzen J., Axer H, Bjerregaard B., von Keyserlingk D.G., (2004), Evolving Rule Based Systems in two Medical Domains Using Genetic Programming, to appear to the AIM Journal, Artificial Intelligence in Medicine, Elsevier Ampazis N., Dounias G., and Jantzen J., (2004), Pap-smear Classification Using Efficient Second Order Neural Network Training Algorithms, Lecture Notes in Artificial Intelligence - Springer (LNAI 3025), pp. 230-245

17. MethodNr. of Cases used & Classes to Discriminate Division of the Data Set to Train & Test Data Training Accuracy Testing AccuracyComprehensibility of the Outcome Entropy Based ML-Results (C4.5 Standard) 500 cases (7 classes) 90 – 10 % (cross. val. 10-folds) 95.4 %70.0 %HIGH Entropy Based ML-Results (C4.5 Boost) 500 (7)90 – 10 % (cross. val. 10-folds) 100 %73.0 %LOW Neuro-Fuzzy Classification (ANFIS Method) 500 (2)Swap-random tests were used for validation ~ 100 %95.5 %MEDIUM Nearest Neighbour Classification (NNH-Method) 500 (2)Swap-random tests were used for validation ~ 100 %96.3 %LOW Standard GP Method for all classes 500 (7)90-10 % (single split) ~ 100 %80.7 %LOW Standard GP for abnormal vs all types of normal cells 500 (5) (5+6+7 unified) 90-10 % (single split) ~ 100 %88.9 %LOW PST-GP: GP-derived Crisp Rule-Based Syst 450 (5) (5+6+7 unified) 50-50 % (single split) 95.6 %91.6 %MEDIUM (Un)supervised HCM / FCM / GK Direct Classification 500 (7)90 – 10 % (cross. val. 10-folds) ~ 100 %72.3 – 77.0 %LOW (Un)supervised HCM / FCM / GK Hierarchical Classification 500 (7)90 – 10 % (cross. val. 10-folds) ~ 100 %79.7 – 80.5 %LOW Supervised FCM 500 (2) (classes 1+2+3+4 vs 5+6+7) 90 – 10 % (cross. val. 10-folds) ~ 100 %96.94 %LOW Feature Selection & Supervised FCM 500 (2) (classes 1+2+3+4 vs 5+6+7) 90 – 10 % (cross. val. 10-folds) ~ 100 %98.36 %LOW LMAM Algorithm 500 (2) (classes 1+2+3+4 vs 5+6+7) 90 – 10 % (cross. val. 10-folds) ~ 100 %98.42 %LOW OLMAM Algorithm 500 (2) (classes 1+2+3+4 vs 5+6+7) 90 – 10 % (cross. val. 10-folds) ~ 100 %98.86 %LOW Results

18. Comments All C.I. methods obtained very high training accuracy and good to excellent testing accuracy (Best performing: LMAM and OLMAM) Most of them had low comprehensibility, ML was the only one with high comprehensibility Most misclassification errors in C4.5 occurred in discrimination between classes 5,6,7 Rule 8: (cover 45) : K/C  0.044 AND KerneLong>8.23 AND CytoLong>52.39 AND KernePeri>27.56 THEN class-3 [0.979] The above rule covers nearly 10% of the training data, no negative instances and there is a prospect for 97.9% probability of correct classification of new cases in the future A rule that discriminates among normal cells belonging to class 4 and abnormal cells belonging to classes 5,6,7 leads to a surprisingly high accuracy (100%), and simplicity KA / CS : nucleus area divided by cytoplasm shortest diameter (if KA / CS  1 then class4 else, if KA / CS > 1 class5,6,7) This rule is familiar to cytopathologists, while it is already used to characterize Class #4 cells (Superficial) between other types of cells and thus, the GP procedure just revealed this criterion If KerneY>CytoMin & KernePeri CytoShort THEN class is INTER (class3) In respect to the classification performance: - it is hard to separate abnormal cells from each other- even for cytopathologists! - columnar cells are sometimes classified as severe dysplastic cells!

19. Automatic screening Capture image Analyse using CHAMP Data analysis Classification Analysis using eyes Classifies using experience

21. Further Work New Danish database consisting of 3000 carefully selected and analyzed cases Practical application of the automated smear classification task is currently elaborated