A Review Of Skin Cancer Detection
DOI:
https://doi.org/10.29304/jqcm.2021.13.1.775Keywords:
lesions, Skin diseases, technology, classification, features, deep learning, CNNAbstract
It can be said that the most common or common health disease is a skin disease. skin diseases are often determined by doctors' experience and sample results (skin biopsy), and it is certainly a time-consuming process. Therefore, there has become an urgent need for an automated system (computer) to identify and discover skin diseases through images with very high accuracy, with fewer doctors or experts in this purview. Identification and classification of the skin disease through the feature/s and characteristics that were taken from these images. Since skin diseases have very similar optical properties And therefore add a lot more challenges to choosing the useful feature/s of the image. This means that the accurate analysis of these skin diseases through images will have a good prognosis, short diagnostic time, and speed in diagnosis, and thus it will facilitate and cost-effective treatment. This paper provides an overview or study on the different methods and techniques for identifying and classifying skin diseases, which are traditional technology and technology based on deep learning.
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References
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[2] https://www.who.int/news-room/q-a-detail/ultraviolet-(uv)-radiation-and-skin-cancer
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[9] https://sites.google.com/site/robustmelanomascreening/dataset
[10] https://www.dropbox.com/s/k88qukc20ljnbuo/PH2Dataset.rar
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[13] http://www.cs.rug.nl/~imaging/databases/melanoma_naevi
[14] https://workshop2019.isic-archive.com/#datasets
[15] Amarathunga, A. A. L. C., Ellawala, E. P. W. C., Abeysekara, G. N., & Amalraj, C. R. J. (2015). Expert system for diagnosis of skin diseases. International Journal of Scientific & Technology Research, 4(01), 174-178.
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[18] Premaladha, J., Sujitha, S., Priya, M. L., & Ravichandran, K. S. (2014). A survey on melanoma diagnosis using image processing and soft computing techniques. Research Journal of Information Technology, 6(2), 65-80.
[19] Lopez, Adria Romero, Xavier Giro-i-Nieto, Jack Burdick, and Oge Marques. "Skin lesion classification from dermoscopic images using deep learning techniques." In 2017 13th IASTED international conference on biomedical engineering (BioMed), pp. 49-54. IEEE, 2017.
[20] Manerkar, M. S., Snekhalatha, U., Harsh, S., Saxena, J., Sarma, S. P., & Anburajan, M. (2016). Automated skin disease segmentation and classification using multi-class SVM classifier.
[21] Barati, E., Saraee, M. H., Mohammadi, A., Adibi, N., & Ahmadzadeh, M. R. (2011). A survey on utilization of data mining approaches for dermatological (skin) diseases prediction. Journal of Selected Areas in Health Informatics (JSHI), 2(3), 1-11.
[22] Kulhalli, Rahul, Chinmay Savadikar, and Bhushan Garware. "A hierarchical approach to skin lesion classification." In Proceedings of the ACM India Joint International Conference on Data Science and Management of Data, pp. 245-250. 2019.
[23] Brinker, Titus Josef, Achim Hekler, Jochen Sven Utikal, Niels Grabe, Dirk Schadendorf, Joachim Klode, Carola Berking, Theresa Steeb, Alexander H. Enk, and Christof von Kalle. "Skin cancer classification using convolutional neural networks: systematic review." Journal of medical Internet research 20, no. 10 (2018): e11936.
[24] Khan, M. A., Javed, M. Y., Sharif, M., Saba, T., & Rehman, A. (2019, April). Multi-model deep neural network based features extraction and optimal selection approach for skin lesion classification. In 2019 international conference on computer and information sciences (ICCIS) (pp. 1-7). IEEE.
[25] Monisha, M., A. Suresh, and M. R. Rashmi. "Artificial intelligence based skin classification using GMM." Journal of medical systems 43.1 (2019): 3.
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[28] Chatterjee, S., Dey, D., Munshi, S., & Gorai, S. (2019). Extraction of features from cross correlation in space and frequency domains for classification of skin lesions. Biomedical Signal Processing and Control, 53, 101581.
[30] Burlina, Philippe M., Neil J. Joshi, Elise Ng, Seth D. Billings, Alison W. Rebman, and John N. Aucott. "Automated detection of erythema migrans and other confounding skin lesions via deep learning." Computers in biology and medicine 105 (2019): 151-156.
[31] Gessert, Nils, et al. "Skin lesion classification using cnns with patch-based attention and diagnosis-guided loss weighting." IEEE Transactions on Biomedical Engineering 67.2 (2019): 495-503.
[32] ur Rehman, M., Khan, S. H., Rizvi, S. D., Abbas, Z., & Zafar, A. (2018, July). Classification of skin lesion by interference of segmentation and convolotion neural network. In 2018 2nd International Conference on Engineering Innovation (ICEI) (pp. 81-85). IEEE.
[33]ur Rehman, M., Khan, S. H., Rizvi, S. D., Abbas, Z., & Zafar, A. (2018, July). Classification of skin lesion by interference of segmentation and convolotion neural network. In 2018 2nd International Conference on Engineering Innovation (ICEI) (pp. 81-85). IEEE
[34]Sun, X., Yang, J., Sun, M., & Wang, K. (2016, October). A benchmark for automatic visual classification of clinical skin disease images. In European Conference on Computer Vision (pp. 206-222). Springer, Cham.
[35]Lopez, Adria Romero, Xavier Giro-i-Nieto, Jack Burdick, and Oge Marques. "Skin lesion classification from dermoscopic images using deep learning techniques." In 2017 13th IASTED international conference on biomedical engineering (BioMed), pp. 49-54. IEEE, 2017
[36]https://www.cancercenter.com/cancer-types/melanoma/symptoms%20
[37]Codella, N., Cai, J., Abedini, M., Garnavi, R., Halpern, A. and Smith, J.R., 2015, October. Deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images. In International workshop on machine learning in medical imaging (pp. 118-126). Springer, Cham.
[2] https://www.who.int/news-room/q-a-detail/ultraviolet-(uv)-radiation-and-skin-cancer
[3] Jana, E., Subban, R., & Saraswathi, S. (2017, December). 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.
[4] https://www.isic-archive.com/#!/topWithHeader/wideContentTop/main
[5] https://www.derm101.com/image-library/?match=IN_
[6] https://www.dermnetnz.org/image-library
[7] https://licensing.edinburgh-innovations.ed.ac.uk/i/software/dermofitimage-library.html
[8] Zhang, Xinyuan, et al. "Towards improving diagnosis of skin diseases by combining deep neural network and human knowledge." BMC medical informatics and decision making 18.2 (2018): 59.
[9] https://sites.google.com/site/robustmelanomascreening/dataset
[10] https://www.dropbox.com/s/k88qukc20ljnbuo/PH2Dataset.rar
[11] Zaqout, I. (2019). Diagnosis of skin lesions based on dermoscopic images using image processing techniques. In Pattern Recognition-Selected Methods and Applications. IntechOpen
[12] Esteva, Andre, et al. "Dermatologist-level classification of skin cancer with deep neural networks." nature 542.7639 (2017): 115-118.
[13] http://www.cs.rug.nl/~imaging/databases/melanoma_naevi
[14] https://workshop2019.isic-archive.com/#datasets
[15] Amarathunga, A. A. L. C., Ellawala, E. P. W. C., Abeysekara, G. N., & Amalraj, C. R. J. (2015). Expert system for diagnosis of skin diseases. International Journal of Scientific & Technology Research, 4(01), 174-178.
[16] https://www.mayoclinic.org/diseases-conditions
[17] Chakraborty, Shouvik, et al. "Image based skin disease detection using hybrid neural network coupled bag-of-features." 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON). IEEE, 2017.
[18] Premaladha, J., Sujitha, S., Priya, M. L., & Ravichandran, K. S. (2014). A survey on melanoma diagnosis using image processing and soft computing techniques. Research Journal of Information Technology, 6(2), 65-80.
[19] Lopez, Adria Romero, Xavier Giro-i-Nieto, Jack Burdick, and Oge Marques. "Skin lesion classification from dermoscopic images using deep learning techniques." In 2017 13th IASTED international conference on biomedical engineering (BioMed), pp. 49-54. IEEE, 2017.
[20] Manerkar, M. S., Snekhalatha, U., Harsh, S., Saxena, J., Sarma, S. P., & Anburajan, M. (2016). Automated skin disease segmentation and classification using multi-class SVM classifier.
[21] Barati, E., Saraee, M. H., Mohammadi, A., Adibi, N., & Ahmadzadeh, M. R. (2011). A survey on utilization of data mining approaches for dermatological (skin) diseases prediction. Journal of Selected Areas in Health Informatics (JSHI), 2(3), 1-11.
[22] Kulhalli, Rahul, Chinmay Savadikar, and Bhushan Garware. "A hierarchical approach to skin lesion classification." In Proceedings of the ACM India Joint International Conference on Data Science and Management of Data, pp. 245-250. 2019.
[23] Brinker, Titus Josef, Achim Hekler, Jochen Sven Utikal, Niels Grabe, Dirk Schadendorf, Joachim Klode, Carola Berking, Theresa Steeb, Alexander H. Enk, and Christof von Kalle. "Skin cancer classification using convolutional neural networks: systematic review." Journal of medical Internet research 20, no. 10 (2018): e11936.
[24] Khan, M. A., Javed, M. Y., Sharif, M., Saba, T., & Rehman, A. (2019, April). Multi-model deep neural network based features extraction and optimal selection approach for skin lesion classification. In 2019 international conference on computer and information sciences (ICCIS) (pp. 1-7). IEEE.
[25] Monisha, M., A. Suresh, and M. R. Rashmi. "Artificial intelligence based skin classification using GMM." Journal of medical systems 43.1 (2019): 3.
[26] https://www.who.int/news-room/q-a-detail/ultraviolet-(uv)-radiation-and-skin-cancer
[27] Ray, Sunil. "Essentials of machine learning algorithms (with python and r codes)." A post at AnalyticsVidhya available at http://www. analyticsvidhya. com/blog/2015/08/common-machine-learning-algorithms (2017).
[28] Chatterjee, S., Dey, D., Munshi, S., & Gorai, S. (2019). Extraction of features from cross correlation in space and frequency domains for classification of skin lesions. Biomedical Signal Processing and Control, 53, 101581.
[30] Burlina, Philippe M., Neil J. Joshi, Elise Ng, Seth D. Billings, Alison W. Rebman, and John N. Aucott. "Automated detection of erythema migrans and other confounding skin lesions via deep learning." Computers in biology and medicine 105 (2019): 151-156.
[31] Gessert, Nils, et al. "Skin lesion classification using cnns with patch-based attention and diagnosis-guided loss weighting." IEEE Transactions on Biomedical Engineering 67.2 (2019): 495-503.
[32] ur Rehman, M., Khan, S. H., Rizvi, S. D., Abbas, Z., & Zafar, A. (2018, July). Classification of skin lesion by interference of segmentation and convolotion neural network. In 2018 2nd International Conference on Engineering Innovation (ICEI) (pp. 81-85). IEEE.
[33]ur Rehman, M., Khan, S. H., Rizvi, S. D., Abbas, Z., & Zafar, A. (2018, July). Classification of skin lesion by interference of segmentation and convolotion neural network. In 2018 2nd International Conference on Engineering Innovation (ICEI) (pp. 81-85). IEEE
[34]Sun, X., Yang, J., Sun, M., & Wang, K. (2016, October). A benchmark for automatic visual classification of clinical skin disease images. In European Conference on Computer Vision (pp. 206-222). Springer, Cham.
[35]Lopez, Adria Romero, Xavier Giro-i-Nieto, Jack Burdick, and Oge Marques. "Skin lesion classification from dermoscopic images using deep learning techniques." In 2017 13th IASTED international conference on biomedical engineering (BioMed), pp. 49-54. IEEE, 2017
[36]https://www.cancercenter.com/cancer-types/melanoma/symptoms%20
[37]Codella, N., Cai, J., Abedini, M., Garnavi, R., Halpern, A. and Smith, J.R., 2015, October. Deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images. In International workshop on machine learning in medical imaging (pp. 118-126). Springer, Cham.
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Published
2021-03-15
How to Cite
Hussein, M. K., & Stephan, J. J. (2021). A Review Of Skin Cancer Detection. Journal of Al-Qadisiyah for Computer Science and Mathematics, 13(1), Comp Page 67 – 76. https://doi.org/10.29304/jqcm.2021.13.1.775
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Computer Articles
