Skin Disease Ensemble Classification Using Transfer Learning and Voting Classifier

Skin Disease Ensemble Classification Using Transfer Learning and Voting Classifier

© 2021 by IJETT Journal
Volume-69 Issue-12
Year of Publication : 2021
Authors : Hari Narayanan A G, Dr. J Amar Pratap Singh
DOI :  10.14445/22315381/IJETT-V69I12P234

How to Cite?

Hari Narayanan A G, Dr. J Amar Pratap Singh, "Skin Disease Ensemble Classification Using Transfer Learning and Voting Classifier," International Journal of Engineering Trends and Technology, vol. 69, no. 12, pp. 287-293, 2021. Crossref,

Recognizing the signs and symptoms of various skin disorders helps make a clinical diagnosis and administering appropriate therapy much easier. Based on a multi-stage feature-processing technique, this study provides a multi-stage feature-processing, and machine learning classifier ensembles approach to skin disease identification. The classifier includes both the Voting Classifier and the Transfer Learning application. The architecture proposed is based on the concept of transfer learning and includes a large number of previously trained deep convolutional neural networks. It is also placed between the post-transfer learning process and the feature selection strategy, assisting us in automatically determining which features are most relevant for prediction and hence which features are most significant for prediction. Following the extraction of the deep features, the classifiers do data analysis. After a large number of classifiers have been deployed, the voting classifier makes predictions about the image`s content. This set of deep features is fed into a huge number of machine learning classifiers, which are then used to forecast the final result. The research uses ISIC`s publicly available skin image datasets to better understand how different types of pre-trained models, machine learning classifiers, and deep feature extractors differ. A study of big skin datasets shows that combining deep features with a voting classifier improves overall performance in the skin classification job.

deep learning, ensemble learning, skin disease classification, machine learning, transfer learning, voting classifier

[1] N. Shahid, T. Rappon and W. Berta, Applications of artificial neural networks in health care organizational decision-making: A scoping review, PLOS ONE, 14(2) (2019).
[2] S. Khan, H. Rahmani, S. Shah, and M. Bennamoun, A Guide to Convolutional Neural Networks for Computer Vision. Morgan & Claypool, (2018) 6-7.
[3] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE, 86(11) (1998) 2278-2324
[4] Xiaojing Yuan, Ning Situ, George Zouridakis in 2008, Automatic segmentation of skin lesions images using evolution strategies from the United States. Science Direct, Biomedical signal processing and control 3 (2008) 220-228.
[5] Aswin. R.B, J. Abdul Jaleel, SibiSalim Implementation of ANN Classifier using MATLAB for Skin Cancer Detection. International Conference on Mobility in Computing- ICMIC13, Organized by Mar Baselios College of Engineering and Technology during December 17-18, 2013at Trivandrum, Kerala, India, (2013) 87 – 94.
[6] Hanzheng Wang, Randy H.Moss, Xiaohe Chen, R.Joe Stanley,William V. Stoecker, M, Emre Celebi, Joseph M. Malters, James M.Grichnik, Ashfaq A. Marghoob, Harold S. Rabinovitz, Scott W. Menzies and Thomas M. Szalapski.Modified Watershed technique and post-processing for segmentation of skin lesions in dermoscopy images.In Journal in Computerized Medical Imaging and Graphics. (2010)
[7] T. Goswami, V. K. Dabhi, and H. B. Prajapati, Skin Disease Classification from Image – A Survey, 2020 6th Int. Conf. Adv. Comput.Commun.Syst.ICACCS,2020,doi: 10.1109/ICACCS48705.2020.9074232. 2020, pp. 599–605
[8] M. A. Al-masni, D. Kim, and T. Kim, Computer Methods and Programs in Biomedicine Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification, Comput. Methods Programs Biomed., 190, 105351, 2020, doi:10.1016/j.cmpb.2020.105351.
[9] R. Sumithra, M. Suhil, and D. S. Guru, Segmentation and classification of skin lesions for disease diagnosis, Procedia Comput. Sci., 45(C) (2015) 76–85, doi:10.1016/j.procs.2015.03.090.
[10] N. Moradi and N. Mahdavi-amiri, Computer Methods and Programs in Biomedicine Kernel sparse representation based model for skin lesions segmentation and classification, Comput.Methods Programs Biomed., 182 (2019) 105038, doi: 10.1016/j.cmpb.2019.105038.
[11] M. A. Marchetti, N. C. Codella, S. W. Dusza, D. A. Gutman, B. Helba,A. Kalloo, N. Mishra, C. Carrera, M. E. Celebi, J. L. De Fazio, and N. Jaimes, Results of the 2016 international skin imaging collaboration international symposium on biomedical imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images, J. Amer. Acad. Dermatol., 78(2) (2018) 270277, doi: 10.1016/j.jaad.2017.08.016.
[12] R. Garnavi, M. Aldeen, and J. Bailey, Computer-aided diagnosis of melanoma using Border- and wavelet-based texture analysis, IEEE Trans. Inf. Technol. Biomed., 16(6) (2012) 12391252, doi: 10.1109/TITB.2012.2212282.
[13] ISIC Project-ISIC Archive. Accessed: May 23, 2019. [Online]. Available:https://www.
[14] J. R. Hagerty, R. J. Stanley, H. A. Almubarak, N. Lama, R. Kasmi, P. Guo, R. J. Drugge, H. S. Rabinovitz, M. Oliviero, and W. V. Stoecker, Deep learning and handcrafted method fusion: Higher diagnostic accuracy for melanoma dermoscopy images, IEEE J. Biomed. Health, 23(4) (2019) 13851391, doi: 10.1109/JBHI.2019.2891049.
[15] DermlS. Accessed: Jun. 9, 2017. [Online]. Available: http://www.
[16] Derm101 Image Library. Accessed: Jan. 12, 2018. [Online]. Available: librarv/
[17] DermNZ-ImageLibrary. Accessed: Jan. 13, 2018. [Online]. Available:
[18] American Cancer Society-Melanoma Skin Cancer. Accessed: Oct.23,2015.[Online].Available: ttp:// documents/the researcherwebcontent/003 120-pdf
[19] Dermnet-Skin Disease Atlas. Accessed: Dec. 31, 2016. [Online]. Available: