Abstract
Skin problem is growing fast all over the country. It is one of the most common types of diseases where some can be painful and some can cause fatal to human life. Everyone should pay attention towards this alarming and emerging health problem which is spreading fast due to numerous reasons like pollution, global warming, ultraviolet rays, etc. To avoid delay in treatment, we have developed a model which will classify the disease using image dataset. The model uses the deep learning approach to get trained for classification. It works on convolutional neural network (CNN) with fine-tuned transfer learning using GoogleNet network. Using pretrained network, the model is trained to classify different skin diseases. The implementation result of training using MATLAB 2018 obtains accuracy of 96.63% with dataset of 4000 images into eight different classes.
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References
Liao, H.: A Deep Learning Approach to Universal Skin Disease Classification. University of Rochester, Department of Computer Science, CSC (2016)
Codella, N.C., Nguyen, Q.B., Pankanti, S., Gutman, D.A., Helba, B., Halpern, A.C., Smith, J.R.: Deep learning ensembles for melanoma recognition in dermoscopy images. IBM J. Res. Dev. 61(4/5), 5-1 (2017)
Muhammad, N.A., Ab Nasir, A., Ibrahim, Z., Sabri, N.: Evaluation of CNN, Alexnet and GoogleNet for fruit recognition. Indones. J. Electr. Eng. Comput. Sci. 12(2), 468–475 (2018)
Coulibaly, S., Kamsu-Foguem, B., Kamissoko, D., Traore, D.: Deep neural networks with transfer learning in millet crop images. Comput. Ind. 108, 115–120 (2019)
Bourouis, A., Zerdazi, A., Feham, M., Bouchachia, A.: M-health: skin disease analysis system using Smartphone’s camera. Procedia Comput. Sci. 19, 1116–1120 (2013)
Ge, Z., Demyanov, S., Chakravorty, R., Bowling, A., Garnavi, R.: Skin disease recognition using deep saliency features and multimodal learning of dermoscopy and clinical images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, September, pp. 250–258. Springer, Cham (2017)
Jaleel, J.A., Salim, S., Aswin, R.B.: Computer aided detection of skin cancer. In: 2013 International Conference on Circuits, Power and Computing Technologies (ICCPCT), March, pp. 1137–1142. IEEE (2013)
Pomponiu, V., Nejati, H., Cheung, N.M.: Deepmole: deep neural networks for skin mole lesion classification. In: 2016 IEEE International Conference on Image Processing (ICIP), September, pp. 2623–2627. IEEE (2016)
Premaladha, J., Ravichandran, K.S.: Novel approaches for diagnosing melanoma skin lesions through supervised and deep learning algorithms. J. Med. Syst. 40(4), 96 (2016)
Han, D., Liu, Q., Fan, W.: A new image classification method using CNN transfer learning and web data augmentation. Expert Syst. Appl. 95, 43–56 (2018)
Alarifi, J.S., Goyal, M., Davison, A.K., Dancey, D., Khan, R., Yap, M.H.: Facial skin classification using convolutional neural networks. In: International Conference Image Analysis and Recognition, July, pp. 479–485. Springer, Cham (2017)
Menegola, A., Fornaciali, M., Pires, R., Bittencourt, F.V., Avila, S., Valle, E.: Knowledge transfer for melanoma screening with deep learning. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), April, pp. 297–300. IEEE (2017)
Codella, N.C., Gutman, D., Celebi, M.E., Helba, B., Marchetti, M.A., Dusza, S.W., Kalloo, A., Liopyris, K., Mishra, N., Kittler, H., Halpern, A.: 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). In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), April, pp. 168–172. IEEE (2018)
Han, S.S., Kim, M.S., Lim, W., Park, G.H., Park, I., Chang, S.E.: Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm. J. Invest. Dermatol. 138(7), 1529–1538 (2018)
Wu, Z., Zhao, S., Peng, Y., He, X., Zhao, X., Huang, K., Wu, X., Fan, W., Chen, M., Li, J., Huang, W., Chen, X., Li, J.: Studies on different CNN algorithms for face skin disease classification based on clinical images. IEEE Access 7 (2019)
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Bhadoria, R.K., Biswas, S. (2020). A Model for Classification of Skin Disease Using Pretrained Convolutional Neural Network. In: Mandal, J., Mukhopadhyay, S. (eds) Proceedings of the Global AI Congress 2019. Advances in Intelligent Systems and Computing, vol 1112. Springer, Singapore. https://doi.org/10.1007/978-981-15-2188-1_14
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DOI: https://doi.org/10.1007/978-981-15-2188-1_14
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