Sheetal JanthakalThis email address is being protected from spambots. You need JavaScript enabled to view it.1 and Girisha Hosalli1

1Department of Computer Science and Engineering Rao Bahadur Y. Mahabaleswarappa Engineering College, Bellary, Karnataka, India


 

Received: November 21, 2021
Accepted: January 25, 2022
Publication Date: March 18, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202301_26(1).0004  


ABSTRACT


Now-a-days, a challenging task in the medical field is the diagnosis of skin illness considering numerous characteristics such as color, size, and the lesion region. Dermoscopy is a technique that has been frequently used to diagnose skin lesions. Researchers have recently demonstrated a keen interest in building an automated diagnosis system, and a satisfying result can be achieved with a high degree of skill, as skin lesion classification necessitates a great deal of knowledge and expertise. Automated skin lesion classification in dermoscopy images is an essential way to improve diagnostic performance. This paper presents the power of convolutional neural networks in classifying the skin lesions into two different categories, namely Granular Parakeratosis and Paraneoplastic Pemphigus. The proposed method includes implementation of Support Vector Machine with hinge loss and linear activation function for classification of lesions and this output is fed to the 10-fold cross validation model, yielding an accuracy of 94%, sensitivity of 93%, and specificity of 91%. The proposed strategy outperforms the SVM kernel Radial basis function (RBF), which was created specifically for binary classification problems.


Keywords: 10-fold Cross Validation, Convolutional Neural Networks, Hinge Loss, Linear Activation Function, Support Vector Machine


REFERENCES


  1. [1] C. Ding, (2015) “Granular Parakeratosis: A Comprehensive Review and a Critical Reappraisal" American Journal of Clinical Dermatology: 6.
  2. [2] A. Akkaya and Y. O, (2015) “Infantile Granular Parakeratosis: Cytologic Examination of Superficial Scrapings as an Aid to Diagnosis" Wiley Periodicals 32(3): 392– 396.
  3. [3] A. Marta Wieczorek, (2016) “Paraneoplastic pemphigus: a short review" Clinical, Cosmetic and Investigational Dermatology: 291–295.
  4. [4] P. M. Pereira and R. F.-P, (2019) “Skin Lesion Classification Enhancement Using Border-Line Features - the Melanoma vs Nevus problem" Biomedical Signal Processing and Control:
  5. [5] M. A. Wahba and A. S. Combined empirical mode decomposition and texture features for skin lesion classification using quadratic support vector machine. en. Health Information Science and Systems, 2017.
  6. [6] T. Geert Litjens, (2017) “A survey on deep learning in medical image analysis" Medical Image Analysis: 60–88.
  7. [7] J. Jaworek-Korjakowska, (2015) “Novel Method for Border Irregularity Assessment in Dermoscopic Color Images" Computational and Mathematical Methods in Medicine: 11.
  8. [8] W.-F. Franz Nachbar, (1994) “The ABCD rule of dermatoscopy: High prospective value in the diagnosis of doubtful melanocytic skin lesions" Journal of the American Academy of Dermatology: 551–559.
  9. [9] I.-H. Alquran Hiam. “The melanoma skin cancer detection and classification using support vector machine”. en. In: 2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies, AEECT 2017. 2017, 1–5.
  10. [10] K. N. Vikranth and K. P. “Classification of Skin diseases using Image processing and SVM”. en. In: Proceedings - International Conference on Vision Towards Emerging Trends in Communication and Networking, ViTECoN 2019. 2019, 1–5.
  11. [11] M. Natale Cascinelli, (1987) “A possible new tool for clinical diagnosis of melanoma: The computer" Journal of the American Academy of Dermatology: 361–367.
  12. [12] R. Amirreza Mahbod. “Skin Lesion Classification Using Hybrid Deep Neural Networks”. en. In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 2019, 1229–1233.
  13. [13] K. Rohan Gaonkar, (2020) “Lesion analysis towards melanoma detection using soft computing techniques" Clinical Epidemiology and Global Health: 501–508.
  14. [14] S. Adi Wibowo. Lightweight encoder-decoder model for automatic skin lesion segmentation. en. Informatics in Medicine Unlocked . 2021.
  15. [15] A. Balazs Harangi, (2020) “Assisted deep learning framework for multi-class skin lesion classification considering a binary classification support" Biomedical Signal Processing and Control:
  16. [16] M. Farhat Afza, (2021) “A hierarchical three-step superpixels and deep learning framework for skin lesion classification" Methods:
  17. [17] H. Ghasem Shakourian Ghalejoogh, (2020) “A Hierarchical Structure based on Stacking Approach for Skin Lesion Classification" Expert Systems with Applications:
  18. [18] G. Boser, (1995) Support Vector Machines: 928–932.
  19. [19] S. Minghe, (2014) “Support Vector Machine Models for Classification" Encyclopedia of Business Analytics and Optimization: 2395–2409.
  20. [20] S. R. D and S. A, (2019) “Deep Learning Based Skin Lesion Segmentation and Classification of Melanoma Using Support Vector Machine (SVM" Asian Pacific Journal of Cancer Prevention: 1555–1561.
  21. [21] C. CORTES and V. V, (1992) “Support-Vector Networks" IEEE Expert-Intelligent Systems and their Applications: 63–72.
  22. [22] N.Z.” “DermNet NZ”. Accessed Oct. 21, 2021. https://dermnetnz.org/. 2021.
  23. [23] N.Z." “Granular parakeratosis images”. Accessed Oct. 21, 2021. https : / / dermnetnz . org / topics / granular-parakeratosis-images?stage=Live. 2021.
  24. [24] J. Lei Bi. “Automatic Melanoma Detection via Multiscale Lesion-biased Representation and Joint Reverse Classification”. en. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI. 2016, 1055–1058.
  25. [25] J. Catarina Barata. “IMPROVING DERMOSCOPY IMAGE ANALYSIS USING COLOR CONSTANCY”. en. In: International Conference on Image Processing(ICIP. 2014, 3527–3531.
  26. [26] Z. Sheng Chen. “A MULTI-TASK FRAMEWORK WITH FEATURE PASSING MODULE FOR SKIN LESION CLASSIFICATION AND SEGMENTATION”. en. In: Proceedings - International Symposium on Biomedical Imaging. 2018, 1126–1129.
  27. [27] A. Hassanien, (2020) “An Indexed Non-probability Skyline Query Processing Framework for Uncertain Data" Advances in Intelligent Systems and Computing:
  28. [28] A. G. Pacheco and A.-R. A, (2019) “Skin cancer detection based on deep learning and entropy to detect outlier samples" ISIC challenge: 1–6.
  29. [29] G. Amirreza Mahbod, (2019) “SKIN LESION CLASSIFICATION USING HYBRID DEEP NEURAL NETWORKS" Ieee: 1229–1233.
  30. [30] A. Balazs Harangi. “Classification of Skin Lesions Using An Ensemble of Deep Neural Networks”. en. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. 2018, 2575–2578.
  31. [31] S. Filali Youssef, (2019) “An improved segmentation approach for skin lesion classification" Statistics, Optimization and Information Computing: 456–467.


    
 

0.9
2021CiteScore
 
 
42nd percentile
Powered by  Scopus

SCImago Journal & Country Rank

Enter your name and email below to receive latest published articles in Journal of Applied Science and Engineering.