A Comprehensive Survey of Convolutional Neural Networks for Skin Cancer Classification and Prediction

Article Sidebar

PDF
Published: Oct 7, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i11s.8085
Keywords:
Skin cancer, Convolutional neural networks (CNNs), Activation functions, Loss functions, Backpropagation, Gradient descent

Main Article Content

Anuj Rapaka
Department of Computer Science and Engineering, Shri Vishnu Engineering College for Women (A), Bhimavaram
Ramesh Babu Mallela
Department of Computer Science and Engineering, Shri Vishnu Engineering College for Women (A), Bhimavaram
M. V. V. Rama Rao
Department of Computer Science and Engineering, Shri Vishnu Engineering College for Women (A), Bhimavaram
Kiran Sree Pokkuluri
Department of Computer Science and Engineering, Shri Vishnu Engineering College for Women (A), Bhimavaram
P. B. V. Raja Rao
Department of Computer Science and Engineering, Shri Vishnu Engineering College for Women (A), Bhimavaram
Rajesh Thammuluri
Department of Computer Science and Engineering, Shri Vishnu Engineering College for Women (A), Bhimavaram

Abstract

Skin cancer, a prevalent and potentially fatal condition, requires early detection and precise classification to ensure effective treatment. In recent years, there has been a significant rise in the popularity of Convolutional Neural Networks (CNNs) prominence as a robust solution for image processing and analysis, significantly surpassing conventional techniques in skin cancer prediction and classification. This survey paper offers a thorough examination of CNNs and their diverse applications in diagnosing skin cancer, emphasizing their benefits, existing obstacles, and potential avenues for future research.

Article Details

How to Cite
Rapaka, A. ., Mallela, R. B. ., Rao, M. V. V. R. ., Pokkuluri, K. S. ., Rao, P. B. V. R. ., & Thammuluri, R. . (2023). A Comprehensive Survey of Convolutional Neural Networks for Skin Cancer Classification and Prediction. International Journal on Recent and Innovation Trends in Computing and Communication, 11(11s), 185–194. https://doi.org/10.17762/ijritcc.v11i11s.8085
Issue
Vol. 11 No. 11s (2023)
Section
Articles

References

Ferlay, J., et al. (2015). Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012. International Journal of Cancer, 136(5), E359-E386.

American Cancer Society. (2021). Skin cancer facts & figures. Retrieved from https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/skin-cancer-facts-and-figures/skin-cancer-facts-and-figures-2021.pdf

Esteva, A., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.

Litjens, G., et al. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60-88.

LeCun, Y., et al. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.

Boureau, Y. L., et al. (2010). Theoretical analysis of feature pooling in visual recognition. Proceedings of the 27th International Conference on Machine Learning (ICML), 111-118.

Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

Nair, V., & Hinton, G. E. (2010). Rectified linear units improve restricted Boltzmann machines. Proceedings of the 27th International Conference on Machine Learning (ICML), 807-814.

Goodfellow, I., et al. (2016). Deep learning. MIT Press.

Rumelhart, D. E., et al. (1988). Learning representations by back-propagating errors. Nature, 323(6088), 533-536.

Esteva, A., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.

Brinker, T. J., et al. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal of Cancer, 113, 47-54.

Liang, X., et al. (2019). Deep learning for automatic localization, identification, and segmentation of anterior segment optical coherence tomography images. Journal of Biomedical Optics, 24(4), 046002.

Haenssle, H. A., et al. (2018). Man against machine: Diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Annals of Oncology, 29(8), 1836-1842.

Tschandl, P., et al. (2020). Human-level artificial intelligence dermatologist equals dermatologist-level artificial intelligence? Development and validation of a deep learning algorithm for the diagnosis of dermal melanoma. Annals of Oncology, 31(6), 806-812.

Codella, N., et al. (2018). "Skin Lesion Analysis toward Melanoma Detection: A Challenge at the 2018 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)". Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.

Kawahara, J., & Hamarneh, G. (2016). "Deep Features to Classify Skin Lesions". Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.

Esteva, A., et al. (2017). "Dermatologist-level classification of skin cancer with deep neural networks". Nature, 542(7639), 115-118.

Kawahara, J., & Hamarneh, G. (2016). "Deep Features to Classify Skin Lesions". Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.

Esteva, A., et al. (2017). "Dermatologist-level classification of skin cancer with deep neural networks". Nature, 542(7639), 115-118.

Kawahara, J., & Hamarneh, G. (2016). "Deep Features to Classify Skin Lesions". Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.

Esteva, A., et al. (2017). "Dermatologist-level classification of skin cancer with deep neural networks". Nature, 542(7639), 115-118.

Kawahara, J., & Hamarneh, G. (2016). "Deep Features to Classify Skin Lesions". Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.

Esteva, A., et al. (2017). "Dermatologist-level classification of skin cancer with deep neural networks". Nature, 542(7639), 115-118.

Kawahara, J., & Hamarneh, G. (2016). "Deep Features to Classify Skin Lesions". Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.

Esteva, A., et al. (2017). "Dermatologist-level classification of skin cancer with deep neural networks". Nature, 542(7639), 115-118.

Codella, N., et al. (2018). "Skin Lesion Analysis toward Melanoma Detection: A Challenge at the 2018 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)". Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.

Kwon, M.-S. ., Park, S.-B. ., Kim, D.-B. ., & Hong, S.-D. . (2023). A Study on Effective Color Production of Outdoor Digital Theme Park. International Journal of Intelligent Systems and Applications in Engineering, 11(4s), 146–149. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2581.

Codella, N., et al. (2018). "Skin Lesion Analysis toward Melanoma Detection: A Challenge at the 2018 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)". Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.

Han, S.S., Kim, M.S., Lim, W. et al. "Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network." PLoS ONE 12(9): e0183913 (2017).

Prof. Madhuri Zambre. (2016). Analysis and Modeling of Physical Stratum for Power Line Communication. International Journal of New Practices in Management and Engineering, 5(01), 08 - 13. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/42

Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. "Grad-CAM: Visual explanations from deep networks via gradient-based localization." In Proceedings of the IEEE International Conference on Computer Vision (ICCV) (pp. 618–626) (2017).

Goar, D. V. . (2021). Biometric Image Analysis in Enhancing Security Based on Cloud IOT Module in Classification Using Deep Learning- Techniques. Research Journal of Computer Systems and Engineering, 2(1), 01:05. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/9

Celebi, M.E., Kingravi, H.A., & Uddin, B. "A methodology for the estimation of skin lesion size from dermoscopic images." Journal of Medical Systems 37(2): 9907 (2013).

V. Ravuri, M. V. Subbarao, T. S. Kumar, T. S. Kavitha, D. R. Sandeep and L. Dangeti, "Multi-Cancer Early Detection and Classification Using Machine Learning Based Approaches," 2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), Bhilai, India, 2023, pp. 1-7, doi: 10.1109/ICAECT57570.2023.10117816.

Mei Chen, Machine Learning for Energy Optimization in Smart Grids , Machine Learning Applications Conference Proceedings, Vol 2 2022.

K. S. Pokkuluri and S. S. S. N. U. D. Nedunuri, "Crop Disease Prediction with Convolution Neural Network (CNN) Augmented with Cellular Automata," The International Arab Journal of Information Technology, vol. 19, no. 5, pp. 765-773, 2022.

N. Silpa, V. V. R. Maheswara Rao, M. V. Subbarao, R. R. Kurada, S. S. Reddy and P. J. Uppalapati, "An Enriched Employee Retention Analysis System with a Combination Strategy of Feature Selection and Machine Learning Techniques," 2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2023, pp. 142-149, doi: 10.1109/ICICCS56967.2023.10142473.