Recognition of Skin Diseases using Deep Neural Network Optimized by Group Teaching Algorithm

Recognition of Skin Diseases using Deep Neural Network Optimized by Group Teaching Algorithm

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2020 by IJETT Journal
Volume-68 Issue-9
Year of Publication : 2020
Authors : Narendra Mohan
DOI :  10.14445/22315381/IJETT-V68I9P216

Citation 

MLA Style: Narendra Mohan  "Recognition of Skin Diseases using Deep Neural Network Optimized by Group Teaching Algorithm" International Journal of Engineering Trends and Technology 68.9(2020):109-120. 

APA Style:Narendra Mohan. Recognition of Skin Diseases using Deep Neural Network Optimized by Group Teaching Algorithm  International Journal of Engineering Trends and Technology, 68(9),109-120.

Abstract
The most commonly occurring diseases among all ages of people are the skin diseases. Every people have different skin conditions and these diseases create dangerous effects on the skin. It is essential to recognize the difference between the skin conditions to identify the diseases at its initial stage and to control them from spreading. This work aims to improve the accuracy of diagnostic systems using Image Processing and classification techniques. Basically, the classification of skin diseases undergoes five phase’s namely pre-processing, image segmentation, feature extraction, feature selection and classification. During pre-processing, the quality of original image gets enhanced and noise is removed. Image segmentation is done using FCM (Fuzzy C Means) combined with morphological operators. Then, the features from the segmented image are extracted using LBP (Local Binary Pattern)-GLCM (Gray Level Co-occurrence Matrix) extraction techniques. Sometimes the extracted features may found in large dimension with relevant and irrelevant features. In order to reduce this, an optimization based feature selection process named BGOA (Binary Grasshopper Optimization Algorithm) is involved before the classification phase. Finally, a deep learning based approach named DNN (Deep Neural Network) is used for classification which is optimized using GTO (Group Teaching Optimization) algorithm. The simulation analysis is carried out with PH2 dataset. The performance metrics like accuracy, specificity, and sensitivity are determined.

Reference

[1] C. Edwards and R. Marks, "Evaluation of biomechanical properties of human skin", Clinics in dermatology, vol. 13, no. 4 (1995), pp. 375-380.
[2] C. Hou, H. Wang, Q. Zhang, Y. Li and M. Zhu, "Highly Conductive, Flexible, and Compressible All?Graphene Passive Electronic Skin for Sensing Human Touch", Advanced materials vol. 26, no. 29 (2014), pp. 5018-5024.
[3] F.Solano, "Melanins: skin pigments and much more—types, structural models, biological functions, and formation routes", New Journal of Science 2014 (2014).
[4] R.P. Braun, H.S. Rabinovitz, M.Oliviero, A.W. Kopf and JH. Saurat, "Dermoscopy of pigmented skin lesions", Journal of the American Academy of Dermatology vol. 52, no. 1 (2005), pp. 109-121.
[5] D.M. Reilly, R. Parslew, G. R. Sharpe, S. Powell, and M. R. Green, "Inflammatory mediators in normal, sensitive and diseased skin types", ACTA DERMATOVENEREOLOGICA-STOCKHOLM- vol. 80, no. 3 (2000), pp. 171-174.
[6] S.A.Birlea, M. Serota and D.A. Norris, "Nonbullous Skin Diseases: Alopecia Areata, Vitiligo, Psoriasis, and Urticaria", In The Autoimmune Diseases, Academic Press, (2020) pp. 1211-1234.
[7] K.E. Kim, D. Cho and H.J. Park, "Air pollution and skin diseases: Adverse effects of airborne particulate matter on various skin diseases", Life sciences vol. 152 (2016), pp. 126-134.
[8] H. Long, G. Zhang, L. Wang and Q. Lu, "Eosinophilic skin diseases: a comprehensive review", Clinical reviews in allergy & immunology vol. 50, no. 2 (2016), pp. 189-213.
[9] L. Cecchi, G. D. Amato and I. Annesi-Maesano, "External exposome and allergic respiratory and skin diseases." Journal of Allergy and Clinical Immunology vol. 141, no. 3 (2018), pp. 846-857.
[10] N. Kumar Verma, M.A.M. van Steensel, P. Prasannan, Z.S.Poh, A.D. Irvine and H.H. Oon, "Common Skin Diseases: Chronic Inflammatory and Autoimmune Disorders", Imaging Technologies and Transdermal Delivery in Skin Disorders (2019), pp. 35-59.
[11] A.K. Verma, S. Pal and S. Kumar, "Prediction of skin disease using ensemble data mining techniques and feature selection method-a comparative study", Applied biochemistry and biotechnology vol. 190, no. 2 (2020), pp. 341-359.
[12] B. Ahmad, M. Usama, C-M. Huang, K. Hwang, M.S. Hossain, and G. Muhammad, "Discriminative Feature Learning for Skin Disease Classification Using Deep Convolutional Neural Network", IEEE Access vol. 8 (2020), pp. 39025-39033.
[13] S. Gulati, and R.K. Bhogal, "Serving the Dermatologists: Skin Diseases Detection", In Information and Communication Technology for Sustainable Development, Springer, Singapore, (2020), pp. 799-822.
[14] J. Yang, X. Wu, J. Liang, X. Sun, M-M. Cheng, P.L. Rosin and L. Wang, "Self-paced balance learning for clinical skin disease recognition", IEEE transactions on neural networks and learning systems (2019).
[15] F. Adjed, S.J.S. Gardezi, F. Ababsa, I. Faye, and S.C. Dass, "Fusion of structural and textural features for melanoma recognition", IET Computer Vision vol. 12, no. 2 (2017),pp. 185-195.
[16] A. Adegun and S. Viriri, "Deep Convolutional Network- Based Framework for Melanoma Lesion Detection and Segmentation", In International Conference on Advanced Concepts for Intelligent Vision Systems,Springer, Cham, (2020), pp. 51-62.
[17] R. Kalam, C. Thomas and M.A.Rahiman, "GAUSSIAN KERNEL BASED FUZZY CMeans CLUSTERING ALGORITHM FOR IMAGE SEGMENTATION", Comput. Sci. Inf. Technol (2016), pp. 47-56.
[18] Y. Shikhar, V.P. Singh and R. Srivastava, "Comparative Analysis of Distance Metrics for Designing an Effective Content-based Image Retrieval System Using Colour and Texture Features", International Journal of Image, Graphics and Signal Processing vol. 9, no. 12 (2017), pp. 58.
[19] T.Sutojo, P.S.Tirajani, C.A.Sari, E.H.Rachmawanto,"CBIR for classification of cow types using GLCMand color features extraction", In 2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE) IEEE, (2017 Nov 1), pp. 182-187.
[20] M. Mafarja, I.Aljarah, H. Faris, A.I.Hammouri, M. Al- ZoubiAla’ and S. Mirjalili, "Binary grasshopper optimisation algorithm approaches for feature selection problems." Expert Systems with Applications vol. 117 (2019), pp. 267-286.
[21] Y. Zhang and Z. Jin, "Group teaching optimization algorithm: A novel metaheuristic method for solving global optimization problems", Expert Systems with Applications vol. 148 (2020), pp. 113246.

Keywords
Skin Disease, Deep Learning, Image Processing, GLCM, Group Teaching Optimization.