Abstract
Skin illness is one of the most common medical problems that can affect people of all ages, from infants to the elderly. As the diagnosis of skin illnesses totally relies on the expertise of professionals, skin biopsy reports are laborious, time-consuming, and subject to subjectivity; thus, it is required to boost diagnostic accuracy and entail less human effort. It can be challenging to categories skin illnesses because of their eerie resemblances. This study investigates several methods for classifying skin illnesses, such as Deep Learning, Support Vector Machine (SVM) and Convolutional neural network (CNN).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Cai, G., Zhu, Y., Wu, Y., Jiang, X., Ye, J., Yang, D.: A multimodal transformer to fuse images and metadata for skin disease classification. Vis. Comput. 1–13, 135 (2022)
Aboulmira, A., Hrimech, H., Lachgar, M.: Comparative study of multiple CNN models for classification of 23 skin diseases. Int. J. Online Biomed. Eng. 18, 127–142 (2022)
Wang, Y.: Computer-aided diagnosis of skin cancer with deep learning: addressing the challenges of practical applications. Doctoral dissertation, University of British Columbia (2022)
Aamodt, E.: Combating class imbalances in image classification-a deep neural network-based method for skin disease classification. Master’s thesis, University of Agder (2022)
Lalli, M., Amutha, S.: Evaluation of feature selection methods for disease survival prediction using proposed entropy-based topsis technique (2021)
Rajpoot, A., Saluja, G., Malsa, N., Gupta, V.: A Pchapter swarm optimization based ANN predictive model for statistical detection of COVID-19. In: Artificial Intelligence in Healthcare, pp. 21–34. Springer, Singapore (2022)
Srinivasu, P.N., SivaSai, J.G., Ijaz, M.F., Bhoi, A.K., Kim, W., Kang, J.J.: Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM. Sensors 21(8), 2852 (2021)
Abunadi, I., Senan, E.M.: Deep learning and machine learning techniques of diagnosis dermoscopy images for early detection of skin diseases. Electronics 10(24), 3158 (2021)
Elngar, A.A., Kumar, R., Hayat, A., Churi, P.: Intelligent system for skin disease prediction using machine learning. J. Phys. Conf. Ser. 1998(1), 012037 (2021)
Fan, J., Kim, J., Jung, I., Lee, Y.: A study on multiple factors affecting the accuracy of multiclass skin disease classification. Appl. Sci. 11(17), 7929 (2021)
Hashmani, M.A., Jameel, S.M., Rizvi, S.S.H., Shukla, S.: An adaptive federated machine learning-based intelligent system for skin disease detection: a step toward an intelligent dermoscopy device. Appl. Sci. 11, 2145 (2021)
Polat, K., Koc, K.O.: Detection of skin diseases from dermoscopy image using the combination of convolutional neural network and one-versus-all. J. Artif. Intell. Syst. 2, 80–97 (2020). https://doi.org/10.33969/AIS.2020.21006
Pham, T.C., Doucet, A., Luong, C.M., Tran, C.T., Hoang, V.D.: Improving skin-disease classification based on customized loss function combined with balanced mini-batch logic and real-time image augmentation. IEEE Access 8, 150725–150737 (2020)
Hameed, N., Shabut, A.M., Ghosh, M.K., Hossain, M.A.: Multi-class multi-level classification algorithm for skin lesions classification using machine learning techniques. Expert Syst. Appl. 141, 112961 (2020)
Pugazhenthi, V., et al.: Skin disease detection and classification. Int. J. Adv. Eng. Res. Sci. (IJAERS) 6(5), 396–400 (2019)
Wu, Z.H.E., et al.: Studies on different CNN algorithms for face skin disease classification based on clinical images. IEEE Access 7, 66505–66511 (2019)
Chaurasia, V., Pal, S.: Skin diseases prediction: binary classification machine learning and multi model ensemble techniques. Res. J. Pharm. Technol. 12(8), 3829–3832 (2019)
Mridha, K., et al.: Plant disease detection using web application by neural network. In: 2021 IEEE 6th International Conference on Computing, Communication and Automation (ICCCA), pp. 130–136 (2021). https://doi.org/10.1109/ICCCA52192.2021.9666354
Adhikary, A., Majumder, K., Chatterjee, S., Shaw, R.N., Ghosh, A.: Machine learning based approaches in the detection of Parkinson’s disease – a comparative study. In: Mekhilef, S., Shaw, R.N., Siano, P. (eds.) ICEEE 2022. LNEE, vol. 894, pp. 774–793. Springer, Singapore (2022). https://doi.org/10.1007/978-981-19-1677-9_68
Alenezi, N.S.A.: A method of skin disease detection using image processing and machine learning. Procedia Comput. Sci. 163, 85–92 (2019)
Okuboyejo, D.A., Olugbara, O.O., Odunaike, S.A.: Automating skin disease diagnosis using image classification. In: Proceedings of the World Congress on Engineering and Computer Science, vol. 2, pp. 850–854, October 2013
Lucius, M., et al.: Deep neural frameworks improve the accuracy of general practitioners in the classification of pigmented skin lesions. Diagnostics 10(11), 969 (2020)
Aydogdu, M.F., Celik, V., Demirci, M.F.: Comparison of three different CNN architectures for age classification. In: 2017 IEEE 11th International Conference on Semantic Computing (ICSC), pp. 372–377. IEEE, January 2017
Ji, Q., Huang, J., He, W., Sun, Y.: Optimized deep convolutional neural networks for identification of macular diseases from optical coherence tomography images. Algorithms 12(3), 51 (2019)
Gupta, V., Bibhu, V.: Deep residual network based brain tumor segmentation and detection with MRI using improved invasive bat algorithm. Multimedia Tools Appl., 1–23 (2022)
Malsa, N., Singh, P., Gautam, J., Srivastava, A., Singh, S.P.: Source of treatment selection for different states of India and performance analysis using machine learning algorithms for classification. In: Soft Computing: Theories and Applications, pp. 235–245. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-4032-5_23
Palimkar, P., Shaw, R.N., Ghosh, A.: Machine learning technique to prognosis diabetes disease: random forest classifier approach. In: Bianchini, M., Piuri, V., Das, S., Shaw, R.N. (eds.) Advanced Computing and Intelligent Technologies. LNNS, vol. 218, pp. 219–244. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-2164-2_19
Ann, A.J., Ruiz, C.: Using deep learning for melanoma detection in dermoscopy images. Int. J. Mach. Learn. Comput. 8(1), 61–68 (2018)
Srinivasan, K., et al.: Performance comparison of deep CNN models for detecting driver’s distraction. CMC-Comput. Mater. Continua 68(3), 4109–4124 (2021)
Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., Liu, C.: A survey on deep transfer learning. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds.) International Conference on Artificial Neural Networks, pp. 270–279. Springer, Cham, October 2018. https://doi.org/10.1007/978-3-030-01424-7_27
Rawat, S.S., Verma, S.K., Kumar, Y.: Infrared small target detection based on non-convex triple tensor factorisation. IET Image Proc. 15(2), 556–570 (2021)
Rawat, S.S., Verma, S.K., Kumar, Y.: Reweighted infrared patch image model for small target detection based on non-convex ℒp-norm minimisation and TV regularisation. IET Image Proc. 14(9), 1937–1947 (2020)
Rawat, S.S., Alghamdi, S., Kumar, G., Alotaibi, Y., Khalaf, O.I., Verma, L.P.: Infrared small target detection based on partial sum minimization and total variation. Mathematics 10(4), 671 (2022)
Rawat, S.S., Singh, S., Alotaibi, Y., Alghamdi, S., Kumar, G.: Infrared target-background separation based on weighted nuclear norm minimization and robust principal component analysis. Mathematics 10(16), 2829 (2022)
Singh, S., et al.: Hybrid models for breast cancer detection via transfer learning technique (2022)
Singh, S.: Deep attention network for pneumonia detection using chest x-ray images (2022)
Malsa, N., Vyas, V., Gautam, J.: RMSE calculation of LSTM models for predicting prices of different cryptocurrencies. Int. J. Syst. Assur. Eng. Manag. (2021).https://doi.org/10.1007/s13198-021-01431-1
Gupta, P., Malsa, N., Saxena, N., Agarwal, S., Singh, S.P.: Short-term load forecasting using parametric and non-parametric approaches. In: Pant, M., Sharma, T.K., Verma, O.P., Singla, R., Sikander, A. (eds.) Soft Computing: Theories and Applications. AISC, vol. 1053, pp. 747–755. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-0751-9_68
Gautam, J., Malsa, N., Gautam, S., Gaur, N.K., Adhikary, P., Pathak, S.: Selecting a family planning method for various age groups of different states in India. In: 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON), pp. 1–6 (2021). https://doi.org/10.1109/GUCON50781.2021.9573825
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Mishra, S. et al. (2023). A Comprehensive Review on Skin Disease Classification Using Convolutional Neural Network and Support Vector Machine. In: Shaw, R.N., Paprzycki, M., Ghosh, A. (eds) Advanced Communication and Intelligent Systems. ICACIS 2022. Communications in Computer and Information Science, vol 1749. Springer, Cham. https://doi.org/10.1007/978-3-031-25088-0_64
Download citation
DOI: https://doi.org/10.1007/978-3-031-25088-0_64
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-25087-3
Online ISBN: 978-3-031-25088-0
eBook Packages: Computer ScienceComputer Science (R0)