Melanoma skin cancer cell detection using image processing: A survey

Research Article
Open access

Melanoma skin cancer cell detection using image processing: A survey

M. Sivaranjani 1 , Sathishkumar V E. 2* , Kogilavani Shanmugavadivel 3 , Malliga Subramanian 4
  • 1 Department of CSE, Paavai Engineering College, Pachal, Namakkal, Tamil Nadu, India    
  • 2 Department of Industrial Engineering, Hanyang University, Seoul, Republic of Korea    
  • 3 Department of AI, Kongu Engineering College, Perundurai, Erode,, Tamil Nadu, India    
  • 4 Department of CSE, Kongu Engineering College, Perundurai, Erode, Tamil Nadu, India    
  • *corresponding author sathishkumar@hanyang.ac.kr
ACE Vol.4
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-55-3
ISBN (Online): 978-1-915371-56-0

Abstract

In the current day, melanoma, basal cell carcinoma, and squamous cell carcinoma remains to be the three most common kinds of skin cancer. The likelihood of survival is exceedingly poor for patients with melanoma, making it the most dangerous type of cancer. The victim's chances of survival can only be boosted by detecting the melanoma cancer cells at an early stage. Modern skin cancer detection equipment classifies cancer cells using different algorithms based on machine learning and deep learning. In-depth learning examines the findings state-by-state and provides an in-depth analysis of the technology research for skin cancer diagnosis.

Keywords:

Artificial Neural Network, Image Processing, Melanoma Skin Cancer, Support Vector Machine

Sivaranjani,M.;E.,S.V.;Shanmugavadivel,K.;Subramanian,M. (2023). Melanoma skin cancer cell detection using image processing: A survey. Applied and Computational Engineering,4,249-254.
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References

[1]. Suganya, R. "An automated computer aided diagnosis of skin lesions detection and classification for dermoscopy images." In 2016 International Conference on Recent Trends in Information Technology (ICRTIT), pp. 1-5. IEEE, 2016.

[2]. Cancer Facts and Statistics. http://www.skincancer.org/ skin-cancer-information/skin-cancer-facts. Chiem, A. Al-Jumpily, and R. N. Khushaba, A Novel Hybrid System for Skin Lesion Detection, Dec 2007.

[3]. Farooq, Muhammad Ali, Muhammad Aatif Mobeen Azhar, and Rana Hammad Raza. "Automatic lesion detection system (ALDS) for skin cancer classification using SVM and neural classifiers." In 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE), pp. 301-308. IEEE, 2016.

[4]. Mhaske, H. R., and D. A. Phalke. "Melanoma skin cancer detection and classification based on supervised and unsupervised learning." In 2013 international conference on Circuits, Controls and Communications (CCUBE), pp. 1-5. IEEE, 2013.

[5]. Sundar, RS Shiyam, and M. Vadivel. "Performance analysis of melanoma early detection using skin lession classification system." In 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT), pp. 1-5. IEEE, 2016.

[6]. Rashad, M. W., & Takruri, M. (2016, December). Automatic non-invasive recognition of melanoma using Support Vector Machines. In 2016 International Conference on Bio-engineering for Smart Technologies (BioSMART) (pp. 1-4). IEEE.

[7]. Lau, Ho Tak, and Adel Al-Jumaily. "Automatically early detection of skin cancer: Study based on nueral netwok classification." In 2009 International Conference of Soft Computing and Pattern Recognition, pp. 375-380. IEEE, 2009.

[8]. Satheesha, T. Y., D. Satyanarayana, M. N. Giriprasad, and K. N. Nagesh. "Detection of melanoma using distinct features." In 2016 3rd MEC International Conference on Big Data and Smart City (ICBDSC), pp. 1-6. IEEE, 2016.

[9]. Jain, Yogendra Kumar, and Megha Jain. "Skin cancer detection and classification using Wavelet Transform and Probabilistic Neural Network." (2012): 250-252.

[10]. M. H. Jafari, S. Samavi, S. M. R. Soroushmehr, H. Mohaghegh, N. Karimi, and K. Najarian, Set of descriptors for skin cancer diagnosis using non-dermoscopic color images,Sept 2016.

[11]. Yu, Lequan, Hao Chen, Qi Dou, Jing Qin, and Pheng-Ann Heng. "Automated melanoma recognition in dermoscopy images via very deep residual networks." IEEE transactions on medical imaging 36, no. 4 (2016): 994-1004.

[12]. R. J. Hijmans and J. van Etten, “Raster: Geographic analysis and modeling with raster data,” R Package Version 2.0-12, Jan. 12, 2012. [Online]. Available: http://CRAN.R- project.org/package=raster

[13]. Csabai, D., K. Szalai, and M. Gyöngy. "Automated classification of common skin lesions using bioinspired features." In 2016 IEEE International Ultrasonics Symposium (IUS), pp. 1-4. IEEE, 2016.

[14]. Takruri, Maen, Maram W. Rashad, and Hussain Attia. "Multi-classifier decision fusion for enhancing melanoma recognition accuracy." In 2016 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA), pp. 1-5. IEEE, 2016.

[15]. Afifi, Shereen, Hamid GholamHosseini, and Roopak Sinha. "A low-cost FPGA-based SVM classifier for melanoma detection." In 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), pp. 631-636. IEEE, 2016.

[16]. Skin Lesion Analysis System for Melanoma Detection with an Effective Hair Segmentation Method Supriya Joseph

[17]. Udrea, Andreea, and George Daniel Mitra. "Generative adversarial neural networks for pigmented and non-pigmented skin lesions detection in clinical images." In 2017 21st international conference on control systems and computer science (CSCS), pp. 364-368. IEEE, 2017.

[18]. Jana, Enakshi, Ravi Subban, and S. Saraswathi. "Research on skin cancer cell detection using image processing." In 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1-8. IEEE, 2017.

Cite this article

Sivaranjani,M.;E.,S.V.;Shanmugavadivel,K.;Subramanian,M. (2023). Melanoma skin cancer cell detection using image processing: A survey. Applied and Computational Engineering,4,249-254.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-55-3(Print) / 978-1-915371-56-0(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.4
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Suganya, R. "An automated computer aided diagnosis of skin lesions detection and classification for dermoscopy images." In 2016 International Conference on Recent Trends in Information Technology (ICRTIT), pp. 1-5. IEEE, 2016.

[2]. Cancer Facts and Statistics. http://www.skincancer.org/ skin-cancer-information/skin-cancer-facts. Chiem, A. Al-Jumpily, and R. N. Khushaba, A Novel Hybrid System for Skin Lesion Detection, Dec 2007.

[3]. Farooq, Muhammad Ali, Muhammad Aatif Mobeen Azhar, and Rana Hammad Raza. "Automatic lesion detection system (ALDS) for skin cancer classification using SVM and neural classifiers." In 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE), pp. 301-308. IEEE, 2016.

[4]. Mhaske, H. R., and D. A. Phalke. "Melanoma skin cancer detection and classification based on supervised and unsupervised learning." In 2013 international conference on Circuits, Controls and Communications (CCUBE), pp. 1-5. IEEE, 2013.

[5]. Sundar, RS Shiyam, and M. Vadivel. "Performance analysis of melanoma early detection using skin lession classification system." In 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT), pp. 1-5. IEEE, 2016.

[6]. Rashad, M. W., & Takruri, M. (2016, December). Automatic non-invasive recognition of melanoma using Support Vector Machines. In 2016 International Conference on Bio-engineering for Smart Technologies (BioSMART) (pp. 1-4). IEEE.

[7]. Lau, Ho Tak, and Adel Al-Jumaily. "Automatically early detection of skin cancer: Study based on nueral netwok classification." In 2009 International Conference of Soft Computing and Pattern Recognition, pp. 375-380. IEEE, 2009.

[8]. Satheesha, T. Y., D. Satyanarayana, M. N. Giriprasad, and K. N. Nagesh. "Detection of melanoma using distinct features." In 2016 3rd MEC International Conference on Big Data and Smart City (ICBDSC), pp. 1-6. IEEE, 2016.

[9]. Jain, Yogendra Kumar, and Megha Jain. "Skin cancer detection and classification using Wavelet Transform and Probabilistic Neural Network." (2012): 250-252.

[10]. M. H. Jafari, S. Samavi, S. M. R. Soroushmehr, H. Mohaghegh, N. Karimi, and K. Najarian, Set of descriptors for skin cancer diagnosis using non-dermoscopic color images,Sept 2016.

[11]. Yu, Lequan, Hao Chen, Qi Dou, Jing Qin, and Pheng-Ann Heng. "Automated melanoma recognition in dermoscopy images via very deep residual networks." IEEE transactions on medical imaging 36, no. 4 (2016): 994-1004.

[12]. R. J. Hijmans and J. van Etten, “Raster: Geographic analysis and modeling with raster data,” R Package Version 2.0-12, Jan. 12, 2012. [Online]. Available: http://CRAN.R- project.org/package=raster

[13]. Csabai, D., K. Szalai, and M. Gyöngy. "Automated classification of common skin lesions using bioinspired features." In 2016 IEEE International Ultrasonics Symposium (IUS), pp. 1-4. IEEE, 2016.

[14]. Takruri, Maen, Maram W. Rashad, and Hussain Attia. "Multi-classifier decision fusion for enhancing melanoma recognition accuracy." In 2016 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA), pp. 1-5. IEEE, 2016.

[15]. Afifi, Shereen, Hamid GholamHosseini, and Roopak Sinha. "A low-cost FPGA-based SVM classifier for melanoma detection." In 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), pp. 631-636. IEEE, 2016.

[16]. Skin Lesion Analysis System for Melanoma Detection with an Effective Hair Segmentation Method Supriya Joseph

[17]. Udrea, Andreea, and George Daniel Mitra. "Generative adversarial neural networks for pigmented and non-pigmented skin lesions detection in clinical images." In 2017 21st international conference on control systems and computer science (CSCS), pp. 364-368. IEEE, 2017.

[18]. Jana, Enakshi, Ravi Subban, and S. Saraswathi. "Research on skin cancer cell detection using image processing." In 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1-8. IEEE, 2017.

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