Analysis Of Skin Cancer Detection Using Svm & Resnet-50
Keywords:
ABCD criteria, Melanoma, Skin cancer, CNN, ANNAbstract
The paper utilizes machine learning algorithms that incorporate Support Vector Machines (SVM) and Resnet-50, in detecting skin cancer from dermoscopy images. The study evaluates the performance of both models using accuracy, confusion matrix, graphs, and Receiver Operating Characteristics (ROC) to determine which model is more effective in skin cancer detection. Previous studies suggest that Resnet-50 outperforms SVM in terms of detection accuracy. Therefore, this paper also demonstrates the potential of combining both models to improve skin cancer detection accuracy. The outcomes of this study hold substantial inference for the field of clinical practice. By using computer-aided diagnosis (CAD) systems, clinicians can make more accurate diagnoses of skin cancer, reducing interobserver variability and improving objectivity. This research underscores the capacity of machine learning models to transform the aspect of skin cancer diagnosis and treatment, ultimately leading to enhanced patient outcomes. The abstract offers valuable perspectives on the efficiency of machine learning models in the realm of skin cancer detection, rendering it a valuable point of reference for researchers and clinicians exploring the usage of machine learning canon in this domain.
Downloads
References
I. K. Pious and R. Srinivasan, "A Review on Early Diagnosis of Skin Cancer Detection Using Deep Learning Techniques," IEEE,2022 International Conference on Computer, Power and Communications (ICCPC), Chennai, India, 2022, pp. 247-253, Doi: 10.1109/ICCPC55978.2022.10072274.
Seeja R and Suresh A2, “Deep Learning Based Skin Lesion Segmentation and Classification of Melanoma Using Support Vector Machine (SVM)”, DOI: 10.31557/APJCP.2019.20.5.1555
He, J., Zhou, L., Yang, C., & Mao, X. (2021). Development of a random forest model for classification of urban land use based on multi-source remote sensing data. ISPRS International Journal of Geo-Information, 10(6), 386. DOI: 10.3390/ijgi10060386
Mohammadreza Eman, Hamid Reza Arabnia & Khaled Rasheed, “A review of deep transfer learning ann recent advancements”, USA 2023; DOI: 10.3390/technologies11020040
Alpaydin, E. (2010),“Introduction to Machine Learning (2nd ed.)”. MIT Press.
Goodfellow, I., Bengio, Y., & Courville, A. (2016),“Deep Learning”, MIT Press.
Bishop, C. M. (2006),“Pattern Recognition and Machine Learning”,Springer.
Sutton, R. S., & Barto, A. G. (2018),“Reinforcement Learning: An Introduction”,MIT Press.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012), “Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems”, (pp. 1097-1105).
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell, "DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition," in Proceedings of the 31st International Conference on Machine Learning, 2014. DOI: .org/10.48550/ arXiv. 1310.1531
K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770-778. DOI: 10.1109/CVPR.2016.90
Vapnik, V. N., & Cortes, C. (1995). Support vector networks. Machine learning, 20(3), 273-297. DOI .org/10.1007/BF00994018
Bennett, K. P., & Campbell, C. (2000). Support vector machines: hype or hallelujah? ACM SIGKDD Explorations Newsletter, 2(2), 1-13. DOI: org/10.1145/380995.380999
Niharika Goud, Amudha J, IEEE "Skin Cancer Detection Using ResNet-50"; DOI: 10.1109/ ICCCA49541. 2020.9250855
Changjian Zhou 1,2, Jia Song1, Sihan Zhou3, Zhiyao Zhang3, And Jinge Xing2, IEEE "COVID-19 Detection Based on Image Regrouping and Resnet-SVM Using Chest X-Ray Images; DOI: 10.1109/ACCESS.2021.3086229
Kaiming He, Jian Sun, “Deep Residual Learning for Image Recognition”, June 2016, DOI:10.1109 /CVPR .2016 .90
Friedman, R. J., Rigel, D. S., & Kopf, A. W. (1985),“Early detection of malignant melanoma: the role of physician examination and self-examination of the skin”CA: A Cancer Journal for Clinicians, 35(3), 130-151. DOI: 10.3322/canjclin.35.3.130.
MacKie, R. M., English, J., Aitchison, T. C., & Fitzsimons, C. P. (1989),“ The number and distribution of benign pigmented moles (melanocytic naevi) in a healthy British population”,The British Journal of Dermatology, 121(6), 675-683. DOI: 10.1111/j.1365-2133.1985.tb02060.x
Tschandl, P., Rosendahl, C. & Kittler, H., “ The HAM10000 dataset, a large collection of multi-sources dermatoscopic images of common pigmented skin lesions.” Sci Data 5, 180161 (2018).
Saeed Alzahrani, Waleed Al-Nuaimy, “Seven-Point Checklist with Convolutional Neural Networks for Melanoma Diagnosis”, October 2019 DOI:10.1109/EUVIP47703.2019.8946208
Linders, M., Binkhorst, M., Draaisma, J.M.T. et al. Adherence to the ABCDE approach in relation to the method of instruction: a randomized controlled simulation study. BMC Emerg Med 21, 121 (2021). DOI: 10.1186/ s12873-021-00509-0
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
