Abstract
Patients diagnosed with skin cancer like melanoma are prone to a high mortality rate. Automatic lesion analysis is critical in skin cancer diagnosis and ensures effective treatment. The computer-aided diagnosis of such skin cancer in dermoscopic images can significantly reduce the clinicians’ workload and help improve diagnostic accuracy. Although researchers are working extensively to address this problem, early detection and accurate identification of skin lesions remain challenging. This research focuses on a two-step framework for skin lesion segmentation followed by classification for lesion analysis. We explored the effectiveness of deep convolutional neural network (CNN) based architectures by designing an encoder-decoder architecture for skin lesion segmentation and CNN based classification network. The proposed approaches are evaluated quantitatively in terms of the Accuracy, mean Intersection over Union(mIoU) and Dice Similarity Coefficient. Our cascaded end-to-end deep learning-based approach is the first of its kind, where the classification accuracy of the lesion is significantly improved because of prior segmentation. The code is available at https://www.github.com/shubhaminnani/skin/lesion.
Authors are greatful to Center of Excellence, Signal and Image Processing, SGGS IET, Nanded, for computing resources.
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
Dermoscopy and mole scans in Perth and regional WA. https://myskincentre.com.au/service/dermoscopy/ (2018). Accessed 20 Feb 2020
Melanoma stats, facts, and figures. https://www.aimatmelanoma.org/about-melanoma/melanoma-stats-facts-and-figures (2018). Accessed 20 Feb 2020
Baheti, B., Innani, S., Gajre, S., Talbar, S.: Eff-unet: A novel architecture for semantic segmentation in unstructured environment. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1473–1481 (2020). https://doi.org/10.1109/CVPRW50498.2020.00187
Baheti, B., Innani, S., Gajre, S., Talbar, S.: Semantic scene segmentation in unstructured environment with modified deeplabv3+. Pattern Recogn. Lett. 138, 223–229 (2020). https://doi.org/10.1016/j.patrec.2020.07.029
Celebi, M.E., et al.: Border detection in dermoscopy images using statistical region merging. Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI). 14, 347–53 (2008). https://doi.org/10.1111/j.1600-0846.2008.00301.x
Celebi, M.E., Wen, Q., Hwang, S., Iyatomi, H., Schaefer, G.: Lesion border detection in dermoscopy images using ensembles of thresholding methods. CoRR abs/1312.7345 (2013). http://arxiv.org/abs/1312.7345
Chollet, F.: Xception: Deep learning with depthwise separable convolutions, pp. 1800–1807, July 2017. https://doi.org/10.1109/CVPR.2017.195
Codella, N.C.F., et al.: 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). CoRR abs/1710.05006 (2017). http://arxiv.org/abs/1710.05006
Codella, N.C.F., et al.: Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (ISIC). CoRR abs/1902.03368 (2019). http://arxiv.org/abs/1902.03368
Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature. 542 (2017). https://doi.org/10.1038/nature21056
González-Díaz, I.: Dermaknet: incorporating the knowledge of dermatologists to convolutional neural networks for skin lesion diagnosis. IEEE J. Biomed. Health Inform. 23(2), 547–559 (2019). https://doi.org/10.1109/JBHI.2018.2806962
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. CoRR abs/1512.03385 (2015). http://arxiv.org/abs/1512.03385
Howard, A.G., et al.: Mobilenets: efficient convolutional neural networks for mobile vision applications (2017)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. CoRR abs/1709.01507 (2017). http://arxiv.org/abs/1709.01507
Innani, S., Dutande, P., Baheti, B., Talbar, S., Baid, U.: Fuse-PN: a novel architecture for anomaly pattern segmentation in aerial agricultural images. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 2954–2962 (2021). https://doi.org/10.1109/CVPRW53098.2021.00331
Jahanifar, M., Zamani Tajeddin, N., Mohammadzadeh Asl, B., Gooya, A.: Supervised saliency map driven segmentation of lesions in dermoscopic images. IEEE J. Biomed. Health Inform. 23(2), 509–518 (2019). https://doi.org/10.1109/JBHI.2018.2839647
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations, December 2014
Oliveira, R.B., Filho, M.E., Ma, Z., Papa, J.P., Pereira, A.S., Tavares, J.M.R.: Computational methods for the image segmentation of pigmented skin lesions: A review. Comput. Methods Programs Biomed. 131, 127–141 (2016). https://doi.org/10.1016/j.cmpb.2016.03.032
Rigel, D.S., Russak, J., Friedman, R.: The evolution of melanoma diagnosis: 25 years beyond the ABCDs. CA: Cancer J. Clin. 60(5), 301–316. https://doi.org/10.3322/caac.20074 ,https://acsjournals.onlinelibrary.wiley.com/doi/abs/10.3322/caac.20074
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Sandler, M., Howard, A.G., Zhu, M., Zhmoginov, A., Chen, L.: Inverted residuals and linear bottlenecks: Mobile networks for classification, detection and segmentation. CoRR abs/1801.04381 (2018). https://arxiv.org/abs/1801.04381
Sultana, F., Sufian, A., Dutta, P.: Evolution of image segmentation using deep convolutional neural network: a survey. arXiv preprint arXiv:2001.04074 (2020)
Tajeddin, N.Z., Asl, B.M.: A general algorithm for automatic lesion segmentation in dermoscopy images. In: 2016 23rd Iranian Conference on Biomedical Engineering and 2016 1st International Iranian Conference on Biomedical Engineering (ICBME), pp. 134–139, November 2016. https://doi.org/10.1109/ICBME.2016.7890944
Tajeddin, N.Z., Asl, B.M.: Melanoma recognition in dermoscopy images using lesion’s peripheral region information. Comput. Methods Programs Biomed. 163, 143–153 (2018). https://doi.org/10.1016/j.cmpb.2018.05.005
Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. CoRR abs/1905.11946 (2019). https://arxiv.org/abs/1905.11946
Tschandl, P., Rosendahl, C., Kittler, H.: Data descriptor: the HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5, 1–9 (2018). https://doi.org/10.1038/sdata.2018.161
Yu, L., Chen, H., Dou, Q., Qin, J., Heng, P.: Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE Trans. Med. Imaging 36(4), 994–1004 (2017). https://doi.org/10.1109/TMI.2016.2642839
Yuan, Y., Lo, Y.: Improving dermoscopic image segmentation with enhanced convolutional-deconvolutional networks. CoRR abs/1709.09780 (2017). https://arxiv.org/abs/1709.09780
Zhang, X., Huang, S., Zhang, X., Wang, W., Wang, Q., Yang, D.: Residual inception: a new module combining modified residual with inception to improve network performance. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 3039–3043, October 2018. https://doi.org/10.1109/ICIP.2018.8451515
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
Innani, S., Dutande, P., Baheti, B., Baid, U., Talbar, S. (2023). Deep Learning Based Novel Cascaded Approach for Skin Lesion Analysis. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1776. Springer, Cham. https://doi.org/10.1007/978-3-031-31407-0_46
Download citation
DOI: https://doi.org/10.1007/978-3-031-31407-0_46
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-31406-3
Online ISBN: 978-3-031-31407-0
eBook Packages: Computer ScienceComputer Science (R0)