FRACTAL MODEL FOR SKIN CANCER DIAGNOSIS USING PROBABILISTIC CLASSIFIERS
DOI:
https://doi.org/10.29284/ijasis.7.1.2021.21-29Keywords:
Skin cancer diagnosis, fractal model, parametric classification, non-parametric classificationAbstract
The early detection of skin cancer can lead to high prognosis rate. Thus it is very important to identify abnormalities in skin as early as possible. However, the detection of abnormalities at their early stages is a challenging task since the shape and colour of the abnormalities vary with different persons. In this study, fractal model for skin cancer diagnosis is developed. Differential Box Counting (DBC) method is implemented to get the fractal dimension from the dermoscopic images from two databases; International Skin Imaging Collaboration (ISIC) and PH2 database. The fractal features are classified using a parametric and non-parametric classification approach. The system provides promising results for skin cancer diagnosis with 96.5% accuracy on PH2 images and 91.5% accuracy on ISIC database images using the non-parametric classifier whereas parametric classifier gives 95% (PH2) and 90% (ISIC) images.
Downloads
References
T.J. Brinker, A. Hekler, J.S. Utikal, N. Grabe, D. Schadendorf, J. Klode and C. Von Kalle, “Skin cancer classification using convolutional neural networks: systematic review”, Journal of medical Internet research, Vol. 20,No. 10, 2018, pp. e11936
A. Esteva, B. Kuprel, R.A. Novoa, J. Ko, S.M. Swetter, H.M. Blau and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks”, Nature, Vol. 542, No. 7639, 2017, pp. 115-118.
U.O. Dorj, K.K. Lee, J.Y. Choi, and M. Lee, “The skin cancer classification using deep convolutional neural network”, Multimedia Tools and Applications, Vol. 77, No. 8, 2018, pp. 9909-9924.
A. Hekler, J.S. Utikal, A.H. Enk, A. Hauschild, M. Weichenthal, R.C. Maron and A. Thiem, “Superior skin cancer classification by the combination of human and artificial intelligence”, European Journal of Cancer, Vol. 120, 2019, pp. 114-121.
M. Anas, K. Gupta, and S. Ahmad, “Skin cancer classification using K-means clustering”, International Journal of Technical Research and Applications, Vol. 5, No.1, 2017, pp. 62-65.
S. Jinnai, N. Yamazaki, Y. Hirano, Y. Sugawara, Y. Ohe and R. Hamamoto, “The development of a skin cancer classification system for pigmented skin lesions using deep learning”, Biomolecules, Vol. 10, No. 8, 2020, pp. 1123.
S.S. Chaturvedi, K. Gupta, and P.S. Prasad, “Skin lesion analyser: an efficient seven-way multi-class skin cancer classification using MobileNet”, In International Conference on Advanced Machine Learning Technologies and Applications, 2020, pp. 165-176.
M.A. Farooq, M.A.M. Azharand and R.H. Raza, “Automatic lesion detection system (ALDS) for skin cancer classification using SVM and neural classifiers”, IEEE 16th International Conference on Bioinformatics and Bioengineering, 2016, pp. 301-308.
T.C. Pham, G.S. Tran, T.P. Nghiem, A. Doucet, C.M. Luong and V.D. Hoang, “A comparative study for classification of skin cancer”, International Conference on System Science and Engineering, 2019, pp. 267-272.
R. Refianti, A.B. Mutiara and R.P. Priyandini, “Classification of melanoma skin cancer using convolutional neural network”, IJACSA, Vol. 10, No. 3, 2019, pp. 409-417.
J. Amin, A. Sharif, N. Gul, M.A. Anjum, M.W. Nisar, F. Azam and S.A.C. Bukhari, “Integrated design of deep features fusion for localization and classification of skin cancer”, Pattern Recognition Letters, Vol. 131, 2020, pp. 63-70.
M. Goyal, T. Knackstedt, S. Yan and S. Hassanpour, “Artificial intelligence-based image classification for diagnosis of skin cancer: Challenges and opportunities”, Computers in Biology and Medicine, 2020, pp. 104065.
B. Mandelbrot. The Fractal Geometry of Nature. W. H. Freeman and Co., 1982.
A. Pentland, “Fractal-based description of natural scenes”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1983, pp. 201-209.
N. Sarkar and B. Chaudhuri, “An efficient approach to estimate fractal dimension of textural images”, Pattern Recognition letters, Vol. 25, 1992, pp. 1035-1041.
T. Mendonça, P.M. Ferreira, J.S. Marques, A.R. Marca and J. Rozeira, “PH2-A dermoscopic image database for research and benchmarking”, 35th Annual International Conference on Engineering in Medicine and Biology Society, 2013, pp. 5437-5440.
N. Codella, D. Gutman, M.E. Celebi, B. Helba, M.A. Marchetti, S. Dusza, A. Kalloo, K. Liopyris, N. Mishra, H. Kittler and A. Halpern, “Skin Lesion Analysis Toward Melanoma Detection”, A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)". arXiv: 1710.05006 [cs.CV] Available: https://arxiv.org/abs/1710.05006
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
