Deep Learning Technique Based State-Of-The-Art in Skin Cancer Detection: A Review
DOI:
https://doi.org/10.13052/jmm1550-4646.19610Keywords:
Skin lesion, melanoma, Machine Learning Algorithms, malignant, skin cancersAbstract
Research of skin cancer images through visual survey and manual evaluation to investigate skin threatening development has always been abnormal. This manual evaluation of skin injuries to recognize melanoma is monotonous as well as somewhat long. With movement in advancement and fast development in computational resources, different AI techniques and significant learning methods have emerged for assessment of clinical pictures most especially the skin lesion images. In late years, AI arising as an innovation equipped for tackling issues connected with horticulture, medical services, business, and soon. To diminish the endanger to human existence we can embrace AI calculations in the medical care area and can foresee the deadliest skin illnesses like dangerous melanoma in beginning phases. The point of the research is to give bits of knowledge about various classifications of skin lesions and strategies executed to arrange and foresee skin diseases and the job of dermatologists while fostering the models, at last gives a general rundown of existing work.
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Rezayi, S.; Mohammadzadeh, N.; Bouraghi, H.; Saeedi, S.; Mohammadpour, A. Timely Diagnosis of Acute Lymphoblastic Leukemia Using Artificial Intelligence- Oriented Deep Learning Methods. Comput. Intell. Neurosci. 2021, 2021, 5478157.
Tufail, A.B.; Ma, Y.K.; Kaabar, M.K.; Martínez, F.; Junejo, A.; Ullah, I.; Khan, R. Deep learning in cancer diagnosis and prognosis prediction: A minireview on challenges, recent trends, and future directions. Comput. Math. Methods Med. 2021, 2021, 9025470.
Li, X.; Jiao, H.; Wang, Y. Edge detection algorithm of cancer image based on deep learning. Bioengineered 2020, 11, 693–707
Alzubaidi, L.; Zhang, J.; Humaidi, A.J.; Al-Dujaili, A.; Duan, Y.; Al-Shamma, O.; Santamaría, J.; Fadhel, M.A.; Al-Amidie, M.; Farhan, L. Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. J. Big Data 2021, 8, 1–74.
Ghods, A.; Cook, D.J. A survey of deep network techniques all classifiers can adopt. Data Min. Knowl. Discov. 2021, 35, 46–87.
Emanuelli, M.; Sartini, D.; Molinelli, E.; Campagna, R.; Pozzi, V.; Salvolini, E.; Simonetti, O.; Campanati, A.; Offidani, A. The double-edged sword of oxidative stress in skin damage and melanoma: From physiopathology to therapeutical approaches. Antioxidants 2022, 11, 612.
Ferlay, J.; Colombet, M.; Soerjomataram, I.; Parkin, D.M.; Piñeros, M.; Znaor, A.; Bray, F. Cancer statistics for the year 2020: An overview. Int. J. Cancer 2021, 149, 778–789.
Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 2021, 71, 209–249.
Abdar, M.; Samami, M.; Mahmoodabad, S.D.; Doan, T.; Mazoure, B.; Hashemifesharaki, R.; Liu, L.; Khosravi, A.; Acharya, U.R.; Makarenkov, V.; et al. Uncertainty quantification in skin cancer classification using three-way decision- based Bayesian deep learning. Comput. Biol. Med. 2021, 135, 104418.
Hosny, K.M.; Kassem, M.A.; Foaud, M.M. Classification of skin lesions using transfer learning and augmentation with Alex-net. PLoS ONE 2019, 14, e0217293.
Khamparia, A.; Singh, P.K.; Rani, P.; Samanta, D.; Khanna, A.; Bhushan, B. An internet of health things-driven deep learning framework for detection and classification of skin cancer using transfer learning. Trans. Emerg. Telecommun. Technol. 2021, 32, e3963.
Nahata, H.; Singh, S.P. Deep learning solutions for skin cancer detection and diagnosis. In Machine Learning with Health Care Perspective; Springer: Cham, Switzerland, 2020; pp. 159–182.
Demir, A.; Yilmaz, F.; Kose, O. Early detection of skin cancer using deep learning architectures: Resnet-101 and inception-v3. In Proceedings of the 2019 Medical Technologies Congress (TIPTEKNO), Izmir, Turkey, 3–5 October 2019; pp. 1–4.
Cassidy, B.; Kendrick, C.; Brodzicki, A.; Jaworek-Korjakowska, J.; Yap, M.H. Analysis of the ISIC image datasets: Usage, benchmarks and recommendations. Med. Image Anal. 2022, 75, 102305.
Abbas, Q.; Ramzan, F.; Ghani, M.U. Acral melanoma detection using dermoscopic images and convolutional neural networks. Vis. Comput. Ind. Biomed. Art 2021, 4, 25.
Reethu, R.; Preetha, D.; Parameshwaran, P.; Sivaparthipan, C. B.; Kalaikumaran, T. A design of smart device for detection of oral cancer using IoT. Int J Res Eng Sci Manag 2020, 3(3), 44–47.
Sayed, G.I.; Soliman, M.M.; Hassanien, A.E. A novel melanoma prediction model for imbalanced data using optimized SqueezeNet by bald eagle search optimization. Comput. Biol. Med. 2021, 136, 104712.
Mijwil, M.M. Skin cancer disease images classification using deep learning solutions. Multimed Tools Appl. 2021, 80, 26255–26271.
Nawaz, M.; Mehmood, Z.; Nazir, T.; Naqvi, R.A.; Rehman, A.; Iqbal, M.; Saba, T. Skin cancer detection from dermoscopic images using deep learning and fuzzy k- means clustering. Microsc. Res. Tech. 2022, 85, 339–351.
Dorj, U.; Lee Ke Choi, J.; Lee, M. The skin cancer classification using deep convolutional neural network. Multimed. Tools Appl. 2018, 77, 9909–9924.
Afza, F.; Sharif, M.; Mittal, M.; Khan, M.A.; Jude Hemanth, D. A hierarchical three- stepsuperpixels and deep learning framework for skin lesion classification. Methods 2022, 202, 88–102.
Hameed, N.; Shabut, A.M.; Ghosh, M.K.; Hossain, M. Multi-class multi-level classification algorithm for skin lesions classification using machine learning techniques. Expert Syst. Appl. 2020, 141, 112961.
Singh, L.; Janghel, R.R.; Sahu, S.P. TrCSVM: A novel approach for the classification of melanoma skin cancer using transfer learning. Data Technol. Appl. 2021, 55, 64–81.
Arshad, M.; Khan, M.A.; Tariq, U.; Armghan, A.; Alenezi, F.; Javed, M.Y.; Aslam, S.M.; Kadry, S. A computer-aided diagnosis system using deep learning for multiclass skin lesion classification. Comput. Intell. Neurosci. 2021, 2021, 9619079
Khan, M.A.; Akram, T.; Zhang, Y.-D.; Sharif, M. Attributes based skin lesion detection and recognition: A mask RCNN and transfer learning-based deep learning framework. Pattern Recognit. Lett. 2021, 143, 58–66.
Abunadi, I.; Senan, E.M. Deep learning and machine learning techniques of diagnosis dermoscopy images for early detection of skin diseases. Electronics 2021, 10, 3158.
Naeem, A.; Farooq, M.S.; Khelifi, A.; Abid, A. Malignant melanoma classification using deep learning: Datasets, performance measurements, challenges and opportunities. IEEE Access 2020, 8, 110575–110597.
Malik, H.; Anees, T. BDCNet: Multi-classification convolutional neural network model for classification of COVID-19, pneumonia, and lung cancer from chest radiographs. Multimed. Syst. 2022, 28, 815–829
Naeem, A.; Anees, T.; Naqvi, R.A.; Loh, W.-K. A comprehensive analysis of recent deep and federated-learning-based methodologies for brain tumor diagnosis. J. Pers. Med. 2022, 12, 275.
Deeba, F.; Kun, S.; Dharejo, F.A.; Zhou, Y. Sparse representation based computed tomography images reconstruction by coupled dictionary learning algorithm. IET Image Process. 2020, 14, 2365–2375.
Zawish, M.; Siyal, A.A.; Ahmed, K.; Khalil, A.; Memon, S. Brain tumor segmentation in MRI images using Chan-Vese technique in MATLAB. In Proceedings of the 2018 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube), Quetta, Pakistan, 12–13 November 2018; pp. 1–6.
Eraslan, G.; Avsec, Ž.;Gagneur, J.; Theis, F.J. Deep learning: New computational modelling techniques for genomics. Nat. Rev. Genet. 2019, 20, 389–403.
Khan, M.A.; Muhammad, K.; Sharif, M.; Akram, T.; Kadry, S. Intelligent fusion- assisted skin lesion localization and classification for smart healthcare. Neural Comput. Appl. 2021, 1–16.
Chaturvedi, S.S.; Tembhurne, J.V.; Diwan, T. A multi-class skin cancer classification using deep convolutional neural networks. Multimed. Tools Appl. 2020, 79, 28477–28498.
Deeba, F.; Kun, S.; Dharejo, F.A.; Zhou, Y. Wavelet-based enhanced medical image super resolution. IEEE Access 2020, 8, 37035–37044.
Dharejo, F.A.; Deeba, F.; Zhou, Y.; Das, B.; Jatoi, M.A.; Zawish, M.; Du, Y.; Wang, X. TWIST-GAN: Towards wavelet transform and transferred GAN for spatio- temporal single image super resolution. ACM Trans. Intell. Syst. Technol. (TIST) 2021, 12, 1–20
Jeny, A.A.; Sakib, A.N.M.; Junayed, M.S.; Lima, K.A.; Ahmed, I.; Islam, B. SkNet: A convolutional neural networks based classification approach for skin cancer classes. In Proceedings of the 2020 23rd International Conference on Computer and Information Technology (ICCIT), Dhaka, Bangladesh, 19–21 December 2020; pp. 1–6.
AAli, S.; Miah, S.; Haque, J.; Rahman, M.; Islam, K. An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models. Mach. Learn. Appl. 2021, 5, 100036.
You, Y.; Zhang, Z.; Hsieh, C.; Demmel, J.; Keutzer, K. Imagenet training in minutes. In Proceedings of the 47th International Conference on Parallel Processing, Eugene, OR, USA, 13–16 August 2018; pp. 1–10.
Quang, N.H. Automatic skin lesion analysis towards melanoma detection. In Proceedings of the 2017 21st Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES), Hanoi, Vietnam, 15–17 November 2017; pp. 106–111.
Amin, J.; Sharif, A.; Gul, N.; Anjum, M.A.; Nisar, M.W.; Azam, F.; Bukhari, S.A.C. Integrated design of deep features fusion for localization and classification of skin cancer. Pattern Recognit. Lett. 2020, 131, 63–70.
Aburaed, N.; Panthakkan, A.; Al-Saad, M.; Amin, S.A.; Mansoor, W. Deep convolutional neural network (DCNN) for skin cancer classification. In Proceedings of the 2020 27th IEEE International Conference on Electronics, Circuits and Systems (ICECS), Glasgow, UK, 23–25 November 2020; pp. 1–4.
Zhang, J.; Xie, Y.; Xia, Y.; Shen, C. Attention residual learning for skin lesion classification. IEEE Trans. Med. Imaging 2019, 38, 2092–2103.
Liu, L.; Mou, L.; Zhu, X.X.; Mandal, M. Automatic skin lesion classification based on mid-level feature learning. Comput. Med. Imaging Graph. 2020, 84, 101765.
Höhn, J.; Hekler, A.; Krieghoff-Henning, E.; Kather, J.N.; Utikal, J.S.; Meier, F.; Gellrich, F.F.; Hauschild, A.; French, L.; Schlager, J.G.; et al. Integrating patient data into skin cancer classification using convolutional neural networks: Systematic review. J. Med. Internet Res. 2021, 23, e20708.
Esteva, A.; Kuprel, B.; Novoa, R.A.; Ko, J.; Swetter, S.M.; Blau, H.M.; Thrun, S. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017, 542, 115–118.
Maron, R.C.; Weichenthal, M.; Utikal, J.S.; Hekler, A.; Berking, C.; Hauschild, A.; Enk, A.H.; Haferkamp, S.; Klode, J.; Schadendorf, D.; et al. Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks. Eur. J. Cancer 2019, 119, 57–65.
Hosny, K.M.; Kassem, M.A.; Fouad, M.M. Classification of skin lesions into seven classes using transfer learning with AlexNet. J. Digit. Imaging 2020, 33, 1325–1334.
Chassagnon, G.; Vakalopolou, M.; Paragios, N.; Revel, M.P. Deep learning: Definition and perspectives for thoracic imaging. Eur. Radiol. 2020, 30, 2021–2030.
Acosta, M.F.J.; Tovar, L.Y.C.; Garcia-Zapirain, M.B.; Percybrooks, W.S. Melanoma diagnosis using deep learning techniques on dermatoscopic images. BMC Med. Imaging 2021, 21, 6.
Brinker, T.J.; Hekler, A.; Enk, A.H.; Berking, C.; Haferkamp, S.; Hauschild, A.; Weichenthal, M.; Klode, J.; Schadendorf, D.; Holland-Letz, T.; et al. Deep neural networks are superior to dermatologists in melanoma imageclassification. Eur. J. Cancer 2019, 119, 11–17.
