Abstract
Health Informatics plays a great role in the recovery of disease. In health informatics system, the information regarding the conditions of various diseases and subsequent remedy regarding application of medicines have been stored in Computer memory. Based on the conditions of sickness of a particular organ of the patient, the status of the disease of that person can be ascertained using the stored information in the computer and accordingly certain measures are advised by the doctor to the patients with the help of a computer. In this paper skin disease detection technique is proposed using image processing techniques and soft computing models. The soft computing models include Fuzzy logic, Artificial Neural Network and Genetic algorithm. Initially image characteristics have been collected from digital images using image processing tools. Soft computing models viz using Fuzzy logic, Artificial Neural Network and Genetic algorithm have been applied on the image characteristics. Based on the minimum value of average error, the particular soft computing model has been selected to produce the estimated value of image characteristics. K means clustering algorithm has been applied to the estimated value of image characteristics to produce optimal number of clusters. Each cluster represents the degree of the disease. Then image processing tool has been applied to the unknown image of a patient to get image characteristics of new image. Now the selected soft computing tool has been applied on the image characteristics of new image to produce the estimated image characteristics. The estimated image characteristics has been compared with the cluster centre value of the clusters as formed based on Euclidian distance function. The cluster centre with minimum distance function value indicates the degree of disease of a patient.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mitra, A.K., Parekh, R.: Automated detection of skin diseases using texture features. Int. J. Eng. Sci. Technol. (IJEST) 3(6), 4801–4808 (2011). ISSN: 0975-5462
Lau, H.T., Al Jumaily, A.: Automatically early detection of skin cancer: study based on neural network classification. Int. J. Eng. Sci. Technol. (IJEST) 3(6), 4801–4808 (2011). ISSN: 0975-5462
Elgamal, M.: Automatic skin cancer images classification. Int. J. Adv. Comput. Sci. Appl. 4(3), 287–294 (2013)
JaseemaYasmin, J.H., Mohamed Sadiq, M.: An improved iterative segmentation algorithm using canny edge detector with iterative median filter for skin lesion border detection. Int. J. Comput. Appl. (IJCA) 50(6), 37–42 (2012). ISSN: 0975–8887
Ercal, F., Moganti, M., Stoecker, W.V., Moss, R.H.: Detection of skin tumor boundaries in color images. IEEE Trans. Med. Imaging 12(3), 1–8 (1993)
Garnavi, R., Aldeen, M., Emre Celebi, M., Bhuiyan, A., Dolianitis, C., Varigo, G.: Automatic segmentation of dermoscopy images using histogram thresholding on optimal color channels. Int. J. Biol. Life Sci. 8(2), 100–109 (2012)
Barman, M., Paul Choudhury, J.: A fuzzy rule base system for the diagnosis of heart disease. Int. J. Comput. Appl. (0975–8887) 57(7), 46–53 (2012)
Barman, M., Paul Choudhury, J.: A framework for selection of membership function using fuzzy rule base system for the diagnosis of heart disease. Int. J. Inf. Technol. Comput. Sci. 11, 62–70(2013). Published Online October 2013 in MECS (http://www.mecs-press.org/). https://doi.org/10.5815/ijitcs.2013.11.07
Siler, W., Buckley, J.J.: Fuzzy Expert Systems and Fuzzy Reasoning, pp. 1–424. Wiley Interscience [11]. http://www.dermnet.com
Amaliah, B., Fatichah, C., Rahmat Widyanto, M.: ABCD feature extraction for melanoma skin cancer diagnosis. Int. J. Med. Imaging 3(2), 1–8 (2010)
Nguyen, N.H., Lee, T.K., Stella Atkins, M.: Segmentation of light and dark hair in dermoscopic images: a hybrid approach using a universal kernel. Int. J. Comput. Technol. 7623, 42–50 (2010). Proc. Of SPIN
Capdehourat, G., Corez, A., Bazzano, A., Muse, P.: Pigmented “skin lesions classification using dermatoscopic images”. Int. J. Comput. Appl. 50(6), 37–42 (2012)
Emre Celebi, M., Iyatomi, H., Schaefer, G., Stoecker, W.V.: Lesion border detection in dermoscopy images. Comput. Med. Imaging Graph. 33, 148–153 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Barman, M., Choudhury, J.P., Biswas, S. (2019). A Frame Work for Detection of the Degree of Skin Disease Using Different Soft Computing Model. In: Mandal, J., Mukhopadhyay, S., Dutta, P., Dasgupta, K. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2018. Communications in Computer and Information Science, vol 1030. Springer, Singapore. https://doi.org/10.1007/978-981-13-8578-0_5
Download citation
DOI: https://doi.org/10.1007/978-981-13-8578-0_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-8577-3
Online ISBN: 978-981-13-8578-0
eBook Packages: Computer ScienceComputer Science (R0)