Journal club presentation

1. Journal club presentation Presenter: Parameshwar R. HegdeYenepoya Research Centre, Y.U.Mangalore Supervisor: Dr. ManjunathShenoyProfessor and H.O.D. Dept. of Dermatology Yenepoya Medical College, Y.U.Mangalore

3. Introduction • Psoriasis is a chronic and irritating skin disease affects about 2-3% of world’s population • Statistics show that 1.02% of Indian population is suffering from psoriasis disease • Generally, psoriasis appears on scalp, elbows, knees, and lower back but it may spread further to all parts of the bodies • For diagnosis and analysis of skin diseased images, dermatologist requires skilled training

4. Aim • To develop a computer aided diagnosis (CAD) system for classification of psoriasis

5. Fig. 1: Flow diagram of proposed CAD system.

6. Data acquisition and preparation • The data are obtained by digitally photographing the patients under the supervision of dermatologist • The data are taken from Sony NEX-5 camera with 350 dpi • These 540 (270 normal + 270 abnormal) image samples are then used for proposed diagnosis system • The preparation of database is done by manually cropping the healthy skin and psoriatic lesion from the images • There are few samples which are fuzzy in nature

7. Data preparation Fig. 2: Abnormal skin samples (first three rows) and fuzzy abnormal samples (last row) .

8. Data preparation Fig. 3: Normal skin samples (first three rows) and fuzzy abnormal samples (last row) .

9. Feature extraction • Totally of 46 features are extracted from the psoriasis skin images and used as an input to the classifier • Texture features gives the information about a particular pattern in an image • There is a great variability between colors of psoriatic lesion and healthy skin, color features are considered • Aggressiveness of psoriatic disease such as redness and chaoticness is considered as features since physicians like to consider this to discriminate psoriatic lesion and healthy skin

10. Table 1: Features extracted for classification. Feature extraction

11. Feature selection • It avoids the redundant and noisy features and thus reduces the dimensionality of the feature space • Feature selection process based on the mean values of features is proposed • The dominance level is directly proportional to the difference between the mean values of each feature of two classes • Support vector machine (SVM) finds the hyper-plane which maximizes the distance between data points of distinct classes in order to classify the nonlinear data

12. Table 2: Classification accuracy for 46 different feature combinations using polynomial kernel order 2 with K = 5, 10 and N = 540 and T = 20. Result

13. Table 3: Classification accuracy for varying N using K = 5, 10 and N with T = 20 and FC15 feature combination. Result

14. Result Fig. 4: Data size (N) vs. Accuracy curve for K = 5, 10 and N with T = 20 and FC15 feature combination.

15. Table 4: Classification performance obtained with proposed method and other classification approach from literature. Discussion

16. Conclusion • The proposed CAD system shows the classification accuracy of 99.53%, 99.66% and 99.81% for 5-fold, 10-fold and Jack Knife protocols respectively

17. Future scope • There are several aspects of this research that can be improved over time: • Evaluation of such methods over larger databases • Improving the ground truth representations can be made more objective leading to better inter-observer variability designs • Though this research paper takes into consideration images from entire body

18. Limitations • Psoriatic lesions compared with only with the normal skin

19. Thank You