Abstract
Skin cancer is classified as one of the most dangerous cancer. Malignant melanoma is one of the deadliest types of skin cancer. Early detection of malignant melanoma is essential for treatment, hence saving lives and can significantly help to achieve full recovery. Current method heavily relies on clinical examination along with supportive methods to reach the correct clinical diagnosis. This paper considers the use of Machine Learning tools in early detection of skin cancer. It also presents the results of the data analysis of Skin Lesions Distribution according to age and gender and localization.
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
References
Alasadi, A.H.H., AL-Safy, B.M.R.: Early Detection and Classification of Melanoma Skin Cancer. LAP LAMBERT Academic Publishing, Sunnyvale (2016)
Albahar, M.A.: Skin lesion classification using convolutional neural network with novel regularizer. IEEE Access 7, 38306–38313 (2019)
Beetner, D.G., Kapoor, S., Manjunath, S., Zhou, X., Stoecker, W.V.: Differentiation among basal cell carcinoma, benign lesions, and normal skin using electric impedance. IEEE Trans. Biomed. Eng. 50(8), 1020–1025 (2003)
Chatterjee S., Dey D., Munshi, S.: Mathematical morphology aided shape, texture and colour feature extraction from skin lesion for identification of malignant melanoma. In: International Conference on Condition Assessment Techniques in Electrical Systems (CATCON), pp. 200–203 (2015)
Chou, S.-T., Chang, W.-L., Chang, C.-T., Hsu, S.-L., Lin, Y.-C., Shih, Y.: Cinnamomum cassia essential oil inhibits α-msh-induced melanin production and oxidative stress in murine b16 melanoma cells. Int. J. Mol. Sci. 14(9), 19186–19201 (2013)
Codella, N.C., et al.: Deep learning ensembles for melanoma recognition in dermoscopy images. IBM J. Res. Dev. 61(4/5), 5–1 (2017)
Darvin M., Klemp M., Weinigel M., Meinke M., König K., Lademann, J.: In vivo imaging for detection and discrimination of actinic keratosis and squamous cell carcinoma from healthy human skin using two-photon tomography. In: International Conference Laser Optics (LO), pp. S2–20 (2016)
De Gruijl, F.: Skin cancer and solar uv radiation. Eur. J. Cancer 35(14), 2003–2009 (1999)
Ekimci, G.D., Onak, G., Karaman, O., Ercan, U.K.: Assessment of direct and fluid-mediated cold atmospheric plasma treatment eficacy on squamous cell carcinoma. In: Medical Technologies Congress (TIPTEKNO), pp. 1–4 (2019)
Eltayef, K.A.A.: Segmentation and lesion detection in dermoscopic images. PhD thesis, Brunel University London (2017)
Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115–118 (2017)
Hripcsak, G., Albers, D.J.: Next-generation phenotyping of electronic health records. J. Am. Med. Inform. Assoc. 20(1), 117–121 (2013)
Hussein, S., Kandel, P., Bolan, C.W., Wallace, M.B., Bagci, U.: Lung and pancreatic tumor characterization in the deep learning era: novel supervised and unsupervised learning approaches. IEEE Trans. Med. Imaging 38(8), 1777–1787 (2019)
Isasi, A.G., Zapirain, B.G., Zorrilla, A.M.: Melanomas non-invasive diagnosis application based on the abcd rule and pattern recognition image processing algorithms. Comput. Biol. Med. 41(9), 742–755 (2011)
Kanimozhi T., Murthi, A.: Computer aided melanoma skin cancer detection using artificial neural network classifier. Singaporean J. Sci. Res. (SJSR), J. Sel. Areas Microelectron. (JSAM) 8(2), 35–42 (2016)
Kasmi, R., Mokrani, K.: Classification of malignant melanoma and benign skin lesions: implementation of automatic abcd rule. IET Image Process.10(6), 448–455 (2016)
Lopez, A.R., Giro-i Nieto, X., Burdick, J., Marques, O.: Skin lesion classification from dermoscopic images using deep learning techniques. In: 13th IASTED International Conference on Biomedical Engineering (BioMed), pp. 49–54 (2017)
Mahendraraj, K., Sidhu, K., Lau, C.S.M., McRoy, G.J., Chamberlain, R.S., Smith, F.O.: Malignant melanoma in African–Americans: a population-based clinical outcomes study involving 1106 African–American patients from the surveillance, epidemiology, and end result (SEER) database (1988–2011). Medicine 96(15), e6258 (2017)
MarÃn, C., Alférez, G.H., Córdova, J., González, V.: Detection of melanoma through image recognition and artificial neural networks. In: Jaffray, D.A. (ed.) World Congress on Medical Physics and Biomedical Engineering, June 7–12, 2015, Toronto, Canada. IP, vol. 51, pp. 832–835. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19387-8_204
Mishra, N.K., Celebi, M.E.: An overview of melanoma detection in dermoscopy images using image processing and machine learning. arXiv preprint arXiv:1601.07843 (2016)
Nasr-Esfahani, E., et al.: Melanoma detection by analysis of clinical images using convolutional neural network. In: 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1373–1376 (2016)
Nayman, T., Bostan, C., Logan, P., Burnier, M.N., Jr.: Uveal melanoma risk factors: a systematic review of meta-analyses. Curr. Eye Res. 42(8), 1085–1093 (2017)
Suo, Q., et al.: Deep patient similarity learning for personalized healthcare. IEEE Trans. Nanobiosci. 17(3), 219–227 (2018)
Waheed, Z., Waheed, A., Zafar, M., Riaz, F.: An efficient machine learning approach for the detection of melanoma using dermoscopic images. In: International Conference on Communication, Computing and Digital Systems (C-CODE), pp. 316–319 (2017)
Wang, Y., Cai, J., Louie, D.C., Lui, H., Lee, T.K., Wang, Z.J.: Classifying melanoma and seborrheic keratosis automatically with polarization speckle imaging. In: IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp. 1–4 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Al-Askar, H., Almurshedi, R., Mustafina, J., Al-Jumeily, D., Hussain, A. (2021). AI in Skin Cancer Detection. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12838. Springer, Cham. https://doi.org/10.1007/978-3-030-84532-2_27
Download citation
DOI: https://doi.org/10.1007/978-3-030-84532-2_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-84531-5
Online ISBN: 978-3-030-84532-2
eBook Packages: Computer ScienceComputer Science (R0)