Abstract
Malignant melanoma is the most severe type of a skin cancer, as it has a tendency to disperse to other regions of a body. This is the reason that mortality rates of melanoma are enormously high. Besides these lamenting facts, it is lightening to know that melanoma can be cured if diagnosed early. For early detection, Computer aided diagnosis systems can be employed as these are non-invasive and highly efficient. These CAD systems are established on concepts of image processing and computer vision. Among all the steps, classification plays a prominent role as detection accuracy directly or indirectly depends on this stage. So in this work, classification of melanocytic and non-melanocytic lesions has been carried out using various individual and ensemble classifiers which include Support vector machine, K nearest neighbors, Decision trees, Random forest, Subspace discriminant, Subspace KNN and RUBoosted Trees. Comparison of results obtained using all these classifiers has been made and it is seen that SVM yields outstanding results by providing accuracy, sensitivity, specificity and AUC of 94.5, 82.5, 97.5 and 0.97 respectively. Performances of various kernels among SVM, KNN, decision trees are evaluated and compared. Other than this, performance based comparison of different ensemble classifiers has also been carried out.
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
ScienceABC. https://www.scienceabc.com/humans/melanin-pigment-definition-meaning-skin-color.html
News Medical Life Sciences. https://www.news-medical.net/health/What-is-Melanin.aspx
Medscape. https://emedicine.medscape.com/article/280245-overview
Livescience. https://www.livescience.com/34783-uv-rays-increase-melanoma-skin-cancer-risk.html
Melanoma Research Alliance. https://www.curemelanoma.org/about-melanoma/melanoma-statistics-2/
Australian Melanoma Research Foundation. http://www.melanomaresearch.com.au/about-melanoma/early-detection.html
Mhaske HR, Phalke DA (2013) Melanoma skin cancer detection and classification based on supervised and unsupervised learning. In: International conference on circuits, controls and communications (CCUBE). IEEE Press, Bengaluru, pp 1–5. https://doi.org/10.1109/ccube.2013.6718539
Mustafa S, Dauda AB, Dauda M (2017) Image processing and SVM classification for melanoma detection. In: International conference on computing networking and informatics (ICCNI). IEEE Press, Lagos, pp 1–5. https://doi.org/10.1109/iccni.2017.8123777
Sundar RSS, Vadivel M (2016) Performance analysis of melanoma early detection using skin lession classification system. In: International conference on circuit, power and computing technologies (ICCPCT). IEEE Press, Nagercoil, pp 1–5. https://doi.org/10.1109/iccpct.2016.7530182
Jiji W, Johnson Durai Raj P (2017) An extensive technique to detect and analyze melanoma: a challenge at the international symposium on biomedical imaging (ISBI)
Chakraborty S, Mali K, Chatterjee S, Banerjee S, Mazumdar KG, Debnath M, Basu P, Bose S, Roy K (2017) Detection of skin disease using metaheuristic supported artificial neural networks. In: 8th annual industrial automation and electromechanical engineering conference (IEMECON). IEEE Press, Bangkok, pp 224–229. https://doi.org/10.1109/iemecon.2017.8079594
Soumya RS, Neethu S, Niju TS, Renjini A, Aneesh RP (2016) Advanced earlier melanoma detection algorithm using colour correlogram. In: 2016 international conference on communication systems and networks (ComNet). Thiruvananthapuram, pp 190–194. https://doi.org/10.1109/csn.2016.7824012
Mustafa S, Kimura A (2018) A SVM-based diagnosis of melanoma using only useful image features. In: 2018 international workshop on advanced image technology (IWAIT). IEEE Press, Chiang Mai, pp 1–4. https://doi.org/10.1109/iwait.2018.8369646
Fonseca-Pinto R, Machado M (2017) A textured scale-based approach to melanocytic skin lesions in dermoscopy. In: 40th international convention on information and communication technology, electronics and microelectronics (MIPRO). IEEE Press, Opatija, pp 279–282. https://doi.org/10.23919/mipro.2017.7973434
Alquran H, Qasmieh IA, Alqudah AM, Alhammouri S, Alawneh E, Abughazaleh A, Hasayen F (2017) The melanoma skin cancer detection and classification using support vector machine. In: IEEE Jordan conference on applied electrical engineering and computing technologies (AEECT). IEEE Press, Aqaba, pp 1–5. https://doi.org/10.1109/aeect.2017.8257738
Arasi MA, El-Horbaty ESM, Salem AM, El-Dahshan ESA (2017) Computational intelligence approaches for malignant melanoma detection and diagnosis. In: 8th international conference on information technology (ICIT). IEEE Press, Amman, pp 55–61. https://doi.org/10.1109/icitech.2017.8079915
Mahmoud H, Abdel-Nasser M, Omer OA (2018) Computer aided diagnosis system for skin lesions detection using texture analysis methods. In: 2018 international conference on innovative trends in computer engineering (ITCE). IEEE Press, Aswan, pp 140–144. https://doi.org/10.1109/itce.2018.8327948
Mendonça TM, Ferreira P, Marques JRS, Marcal A, Rozeira J (2013) PH2—A dermoscopic image database for research and benchmarking. In: 35th international conference of the IEEE engineering in medicine and biology society. IEEE Press, Osaka, pp 3–7. https://doi.org/10.1109/embc.2013.6610779
Lee T, Ng V, Gallagher R, Coldman A, McLean D (1997) DullRazor: a software approach to hair removal from images. Comput Biol Med 27:533–543
Jain S, Jagtap V, Pise N (2015) Computer aided melanoma skin cancer detection using image processing. In: International conference on computer, communication and convergence (ICCC 2015), Procedia Computer Science, pp 735–740. https://doi.org/10.1016/j.procs.2015.04.209
Firmansyah HR, Kusumaningtyas EM, Hardiansyah FF (2017) Detection melanoma cancer using ABCD rule based on mobile device. In: International electronics symposium on knowledge creation and intelligent computing (IES-KCIC). IEEE Press, Surabaya, pp 127–131. https://doi.org/10.1109/kcic.2017.8228575
Kolkur S, Kalbande DR (2016) Survey of texture-based feature extraction for skin disease detection. In: 2016 international conference on ICT in business industry & government (ICTBIG). IEEE Press, Indore, pp 1–6. https://doi.org/10.1109/ictbig.2016.7892649
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gulati, S., Bhogal, R.K. (2020). Detection of Malignant Melanoma Using Various Classifiers. In: Kumar, A., Paprzycki, M., Gunjan, V. (eds) ICDSMLA 2019. Lecture Notes in Electrical Engineering, vol 601. Springer, Singapore. https://doi.org/10.1007/978-981-15-1420-3_46
Download citation
DOI: https://doi.org/10.1007/978-981-15-1420-3_46
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1419-7
Online ISBN: 978-981-15-1420-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)