Contact Us Search Paper

Detection of Skin Diseases from Dermoscopy Image Using the combination of Convolutional Neural Network and One-versus-All

Kemal Polat1,*, Kaan Onur Koc2

Corresponding Author:

Kemal Polat

Affiliation(s):

1. Department of Electrical and Electronics Engineering, Faculty of Engineering, Bolu Abant Izzet Baysal University, 14280, Bolu, Turkey, Email: [email protected]
2. Graduate School of Natural Sciences, Department of Electrical and Electronics Engineering, Bolu Abant Izzet Baysal University, 14280, Bolu, Turkey, Email: [email protected]
*Corresponding Author

Abstract:

The use of deep learning in the field of image processing is increasing. In this study, a new method based on Convolutional Neural Network is proposed to detect skin diseases automatically from Dermoscopy images. Skin cancer is one of the common diseases in the community. In this article, the skin images were taken from the data HAM10000 dataset prepared by Philipp Tschandl. There are seven classes in the skin disease data set; Actinic keratoses and intraepithelial carcinoma, Basal cell carcinoma, Benign keratosis, Dermatofibroma, Melanoma, Melanocytic type and Vascular lesions. To classify the skin diseases automatically, two different methods have been proposed: i) Alone Convolutional Neural Network model, and ii) the combination of Convolutional Neural Network and one- versus- all. In the proposed method, we have not used any pre-processing method to classify them. The raw dermatology images taken from the dataset have been given to the input of Convolutional Neural Network and then trained and tested by these images. In the second proposed method, seven different models having two-classes have been composed and then combined with the one-versus-all approach. While alone, Convolutional Neural Network obtained 77% classification accuracy in the detection of skin disease with seven classes, the combination of Convolutional Neural Network and one-versus-all approach achieved 92.90% accuracy. The obtained results have shown that the proposed method is very promising in the classification of skin disease from Dermoscopy images.

Keywords:

Deep Learning, Skin Cancer, Convolutional Neural Network, Artificial Neural Networks, Image Processing

Downloads: 857 Views: 1576
Cite This Paper:

Kemal Polat, Kaan Onur Koc (2020). Detection of Skin Diseases from Dermoscopy Image Using the combination of Convolutional Neural Network and One-versus-All. Journal of Artificial Intelligence and Systems, 2, 80–97. https://doi.org/10.33969/AIS.2020.21006.

References:

[1] Halk Sağlığı Genel Müdürlüğü-https://hsgm.saglik.gov.tr, (last accessed: January, 2020).
[2] Oktay Yıldız, Melanoma detection from dermoscopy images with deep learning methods: Acomprehensive study, Journal of the Faculty of Engineering and Architecture of Gazi University 34:4 (2019) 2241-2260.
[3] M. Emre Celebi, Hassan A. Kingravi, Bakhtiyar Uddin, Hitoshi Iyatomi, Y. Alp Aslandogan, William V. Stoecker, Randy H. Moss, A methodological approach to the classification of dermoscopy images, Computerized Medical Imaging and Graphics, 31(6), 2007, 362-373.
[4] https://dermnetnz.org/topics/convolutional-neural-networks-in-dermatology/, (last accessed: January, 2020).
[5] Suhail M. Odeh, Abdel Karim Mohamed Baareh, A comparison of classification methods as diagnostic system: A case study on skin lesions, Computer Methods and Programs in Biomedicine, 137, 2016, 311-319.
[6] Fekrache Dalila, Ameur Zohra, Kasmi Reda, Cherifi Hocine, Segmentation and classification of melanoma and benign skin lesions, Optik, 140, 2017, 749-761.
[7] Wiem Abbes, Dorra Sellami, Automatic Skin Lesions Classification Using Ontology-Based Semantic Analysis of Optical Standard Images,Procedia Computer Science, 112, 2017, 2096-2105.
[8] Pedro Pedrosa Rebouças Filho, Solon Alves Peixoto, Raul Victor Medeiros da Nóbrega, D. Jude Hemanth, Aldisio Gonçalves Medeiros, Arun Kumar Sangaiah, Victor Hugo C. de Albuquerque, Automatic histologically-closer classification of skin lesions, Computerized Medical Imaging and Graphics, 68, 2018, 40-54.
[9] Jose Luis Garcia-Arroyo, Begonya Garcia-Zapirain, Segmentation of skin lesions in dermoscopy images using fuzzy classification of pixels and histogram thresholding, Computer Methods and Programs in Biomedicine, 168, 2019, 11-19.
[10] Sertan Serte, Hasan Demirel, Gabor wavelet-based deep learning for skin lesion classification, Computers in Biology and Medicine, 113, 2019, 103423.
[11] Saptarshi Chatterjee, Debangshu Dey, Sugata Munshi, Integration of morphological preprocessing and fractal based feature extraction with recursive feature elimination for skin lesion types classification, Computer Methods and Programs in Biomedicine, 178, 2019, 201-218.
[12] Tschandl, P., Rosendahl, C. & Kittler, H. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci Data 5, 180161 (2018). https://doi.org/10.1038/sdata.2018.161.
[13] Skin Lesions Classification Using Deep Learning Based on Dilated Convolution Aminur Rab Ratul, M. Hamed Mozaffari, Won-Sook Lee, Enea Parimbelli bioRxiv 860700; doi: https://doi.org/10.1101/860700.
[14] Philipp Tschandl, Christoph Sinz, Harald Kittler, Domain-specific classification-pretrained fully convolutional network encoders for skin lesion segmentation, Computers in Biology and Medicine, 104, 2019, 111-116.
[15] Goodfellow, I., Bengio, Y. and Courville, A. “Deep Learning, The MIT Press” (2016).
[16] Aleksey Nozdryn-Plotnicki, Jordan Yap, and William Yolland “Ensembling Convolutional Neural Networks for Skin Cancer Classification“ (2018), https://arxiv.org/abs/1808.05071.
[17] Nils Gessert, Thilo Sentkerac, Frederic Madestaac, Rudiger Schmitz ¨ ad, Helge Kniepag, Ivo Baltruschataef, Rene Werner ´ ac and Alexander Schlaeferb “ Skin Lesion Diagnosis using Ensembles, Unscaled Multi-Crop Evaluation and Loss Weighting” (2018).
[18] Jiaxin Zhuang, Weipeng Li, Siyamalan Manivannan,  Roy Wang, JianGuo Zhang, Jihan Liu, Jiahui Pan, Gongfa Jiang, Ziyu Yin “Skin Lesion Analysis Towards Melanoma Detection Using Deep Neural Network Ensemble ” (2018)
[19] Mohammed K. Amro, Baljit Singh, and Avez Rizvi “Skin Lesion Classification and Segmentation for Imbalanced Classes using Deep Learning ” (2018)
[20] Yeong Chan Lee, Sang-Hyuk Jung, and Hong-Hee Won “ WonDerM: Skin Lesion Classification with Fine-tuned Neural Networks ” (2018).
[21] Nazia Hameed, Antesar M. Shabut, Miltu K. Ghosh, M.A. Hossain, Multi-class multi-level classification algorithm for skin lesions classification using machine learning techniques, Expert Systems with Applications, 141, 2020, 112961.
[22] Pedro M.M. Pereira, Rui Fonseca-Pinto, Rui Pedro Paiva, Pedro A.A. Assuncao, Luis M.N. Tavora, Lucas A. Thomaz, Sergio M.M. Faria, Skin lesion classification enhancement using border-line features – The melanoma vs nevus problem, Biomedical Signal Processing and Control, 57, 2020, 101765.
[23] Javeria Amin, Abida Sharif, Nadia Gul, Muhammad Almas Anjum, Muhammad Wasif Nisar, Faisal Azam, Syed Ahmad Chan Bukhari, Integrated design of deep features fusion for localization and classification of skin cancer, Pattern Recognition Letters, 131, 2020, 63-70.
[24] Ghasem Shakourian Ghalejoogh, Hussain Montazery Kordy, Farideh Ebrahimi, A hierarchical structure based on Stacking approach for skin lesion classification, Expert Systems with Applications, 145, 2020, 113127.
[25] Muhammad Attique Khan, Muhammad Sharif, Tallha Akram, Syed Ahmad Chan Bukhari, Ramesh Sunder Nayak, Developed Newton-Raphson based deep features selection framework for skin lesion recognition, Pattern Recognition Letters, 129, 2020, 293-303.
[26] Fengying Xie, Jiawen Yang, Jie Liu, Zhiguo Jiang, Yushan Zheng, Yukun Wang, Skin lesion segmentation using high-resolution convolutional neural network, Computer Methods and Programs in Biomedicine, 186, 2020, 105241.
[27] Ahmed Refaat Hawas, Yanhui Guo, Chunlai Du, Kemal Polat, Amira S. Ashour, OCE-NGC: A neutrosophic graph cut algorithm using optimized clustering estimation algorithm for dermoscopic skin lesion segmentation, Applied Soft Computing, 86, 2020, 105931.
[28] Teck Yan Tan, Li Zhang, Chee Peng Lim, Adaptive melanoma diagnosis using evolving clustering, ensemble and deep neural networks,Knowledge-Based Systems, 187, 2020, 104807.
[29] M. Nasir, M. Attique Khan, M. Sharif, I.U. Lali, T. Saba, T. Iqbal, An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach, Microscopy Research and Technique, 81 (6) (2018), pp. 528-543, 10.1002/jemt.23009
[30] L. Zhang, G. Yang, X.J. Ye, Automatic skin lesion segmentation by coupling deep fully convolutional networks and shallow network with textons, Journal of Medical Imaging, 6 (2) (2019).