Abstract
Cancer incidence is usually relatively low, skin diseases are not paid enough attention, and most patients, when admitted to the hospital, are in a late state which is already much damage and making it difficult to treat. Some diseases have so many similarities that it is difficult to distinguish between diseases when viewed with the naked eye. Currently, the trends of applying artificial intelligence techniques to support medical imaging diagnosis are vigorously applied and achieved many achievements with deep learning in image recognition. Deep architectures are complex and heavy, while shallow architectures also bring good performance with some appropriate configurations. This study investigates configurations of shallow convolutional neural networks for binary classification tasks to support skin disease diagnosis. Our work focuses on studying and evaluating the effectiveness of simple architectures with high accuracy on the problem of skin disease identification through images. The experiments on eight considered skin diseases have revealed that shallow architectures can perform better on small image sizes (32 \(\times \) 32) rather than larger ones (128 \(\times \) 128) with more than 0.75 in accuracy on all considered diseases.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Skin Cancer Information. https://www.skincancer.org/skin-cancer-information/
Green, A., et al.: Daily sunscreen application and betacarotene supplementation in prevention of basal-cell and squamous-cell carcinomas of the skin: a randomised controlled trial. The Lancet 354(9180), 723–729 (1999). https://doi.org/10.1016/s0140-6736(98)12168-2
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2016). https://doi.org/10.1109/cvpr.2016.90
Li, L.F., Wang, X., Hu, W.J., **ong, N.N., Du, Y.X., Li, B.S.: Deep learning in skin disease image recognition: a review. IEEE Access 8, 208264–208280 (2020). https://doi.org/10.1109/access.2020.3037258
Hameed, N., Ruskin, A., Hassan, K.A., Hossain, M.: A comprehensive survey on image-based computer aided diagnosis systems for skin cancer. In: 2016 10th International Conference on Software, Knowledge, Information Management Applications (SKIMA). IEEE (2016). https://doi.org/10.1109/skima.2016.7916221
Hameed, N., Shabut, A., Hossain, M.A.: A computer-aided diagnosis system for classifying prominent skin lesions using machine learning. In: 2018 10th Computer Science and Electronic Engineering (CEEC). IEEE (2018). https://doi.org/10.1109/ceec.2018.8674183
Stanley, R.J., Stoecker, W.V., Moss, R.H.: A relative color approach to color discrimination for malignant melanoma detection in dermoscopy images. Skin Res. Technol. 13(1), 62–72 (2007). https://doi.org/10.1111/j.1600-0846.2007.00192.x
Goceri, E.: Skin disease diagnosis from photographs using deep learning. In: Tavares, J.M.R.S., Natal Jorge, R.M. (eds.) VipIMAGE 2019. LNCVB, vol. 34, pp. 239–246. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32040-9_25
Kumar, V.B., Kumar, S.S., Saboo, V.: Dermatological disease detection using image processing and machine learning. In: 2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR). IEEE (2016). https://doi.org/10.1109/icaipr.2016.7585217
He, X., Wang, Y., Zhao, S., Chen, X.: Co-attention fusion network for multimodal skin cancer diagnosis. Pattern Recogn. 133, 108990 (2023). https://doi.org/10.1016/j.patcog.2022.108990
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
Pham, N.H., Nguyen, H.T., Phan, T.T. (2023). Shallow Convolutional Neural Network Configurations for Skin Disease Diagnosis. In: Dao, NN., Thinh, T.N., Nguyen, N.T. (eds) Intelligence of Things: Technologies and Applications. ICIT 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 187. Springer, Cham. https://doi.org/10.1007/978-3-031-46573-4_34
Download citation
DOI: https://doi.org/10.1007/978-3-031-46573-4_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-46572-7
Online ISBN: 978-3-031-46573-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)