Using Deep Learning Architectures For Skin Cancer Classification

Abstract

Since skin cancer is one of the most common types of cancer, prompt diagnosis is essential to successful treatment. Impressive performance in image-based classification tasks has been demonstrated by convolutional neural networks (CNNs), particularly in recent years. In this study, the proposed CNN model was applied to the ISIC skin cancer classification challenge. A proposed deep learning model and four popular deep CNN models (ResNet, GoogleNet, AlexNet, and VGG16) were used to classify the skin cancer images. High levels of accuracy on test data from the ISIC dataset were achieved by the proposed CNN model, according to experimental results. Preprocessing was performed on images with sizes of 64x64, 100x100, 224x224, and 128x128 pixels. The experimental results show that the proposed CNN model achieved the highest accuracy rate of 86.76% on 128x128 size images.

Keywords

• Classification
• Convolutional neural network (CNN)
• Deep learning (DL)
• Skin Cancer
• ISIC dataset

References

1. 1. Leiter, U., U. Keim, and C. Garbe, Epidemiology of skin cancer: update 2019. Sunlight, Vitamin D and Skin Cancer, 2020: p. 123-139.
2. 2. Narayanamurthy, V., et al., Skin cancer detection using non-invasive techniques. RSC advances, 2018. 8(49): p. 28095-28130.
3. 3. Singer, S., et al., Gender identity and lifetime prevalence of skin cancer in the United States. JAMA dermatology, 2020. 156(4): p. 458-460.
4. 4. Trager, M.H., et al., Biomarkers in melanoma and non‐melanoma skin cancer prevention and risk stratification. Experimental dermatology, 2022. 31(1): p. 4-12.
5. 5. Siegel, R.L., K.D. Miller, and A. Jemal, Cancer statistics, 2018. CA: a cancer journal for clinicians, 2018. 68(1): p. 7-30.
6. 6. Jones, O., et al., Dermoscopy for melanoma detection and triage in primary care: a systematic review. BMJ open, 2019. 9(8): p. e027529.
7. 7. Phillips, M., et al., Detection of malignant melanoma using artificial intelligence: an observational study of diagnostic accuracy. Dermatology practical & conceptual, 2020. 10(1).
8. 8. Vestergaard, M., et al., Dermoscopy compared with naked eye examination for the diagnosis of primary melanoma: a meta‐analysis of studies performed in a clinical setting. British Journal of Dermatology, 2008. 159(3): p. 669-676.

9. 9. Carli, P., et al., Addition of dermoscopy to conventional naked-eye examination in melanoma screening: a randomized study. Journal of the American Academy of Dermatology, 2004. 50(5): p. 683-689.
10. 10. Al-Masni, M.A., D.-H. Kim, and T.-S. Kim, Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification. Computer methods and programs in biomedicine, 2020. 190: p. 105351.
11. 11. Hasan, M.K., et al., Dermo-DOCTOR: A framework for concurrent skin lesion detection and recognition using a deep convolutional neural network with end-to-end dual encoders. Biomedical Signal Processing and Control, 2021. 68: p. 102661.
12. 12. Hasan, M.K., et al., DSNet: Automatic dermoscopic skin lesion segmentation. Computers in biology and medicine, 2020. 120: p. 103738.
13. 13. Esteva, A., et al., Dermatologist-level classification of skin cancer with deep neural networks. nature, 2017. 542(7639): p. 115-118.
14. 14. Marchetti, M.A., et al., Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images. Journal of the American Academy of Dermatology, 2018. 78(2): p. 270-277. e1.
15. 15. Haenssle, H.A., et al., Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Annals of oncology, 2018. 29(8): p. 1836-1842.
16. 16. Brinker, T.J., et al., A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task. European Journal of Cancer, 2019. 111: p. 148-154.
17. 17. Brinker, T.J., et al., Skin cancer classification using convolutional neural networks: systematic review. Journal of medical Internet research, 2018. 20(10): p. e11936.
18. 18. İnik, Ö., et al., A new method for automatic counting of ovarian follicls on whole slide histological images based on convolutional neural network. Computers in biology and medicine, 2019. 112: p. 103350.
19. 19. Celik, M. and O. Inik, Development of hybrid models based on deep learning and optimized machine learning algorithms for brain tumor Multi-Classification. Expert Systems with Applications, 2024. 238: p. 122159.
20. 20. Inik, O., et al., Prediction of Soil Organic Matter with Deep Learning. Arabian Journal for Science and Engineering, 2023. 48(8): p. 10227-10247.
21. 21. King, G. and L. Zeng, Logistic regression in rare events data. Political analysis, 2001. 9(2): p. 137-163.
22. 22. Zhu, M., et al., Class weights random forest algorithm for processing class imbalanced medical data. IEEE Access, 2018. 6: p. 4641-4652.
23. 23. Han, H., W.-Y. Wang, and B.-H. Mao. Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. in International conference on intelligent computing. 2005. Springer.
24. 24. He, H. and E.A. Garcia, Learning from imbalanced data. IEEE Transactions on knowledge and data engineering, 2009. 21(9): p. 1263-1284.
25. 25. LemaÃŽtre, G., F. Nogueira, and C.K. Aridas, Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning. Journal of machine learning research, 2017. 18(17): p. 1-5.
26. 26. Ramentol, E., et al., Smote-rs b*: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using smote and rough sets theory. Knowledge and information systems, 2012. 33: p. 245-265.
27. 27. Dong, Q., S. Gong, and X. Zhu, Imbalanced deep learning by minority class incremental rectification. IEEE transactions on pattern analysis and machine intelligence, 2018. 41(6): p. 1367-1381.
28. 28. Mariani, G., et al., Bagan: Data augmentation with balancing gan. arXiv preprint arXiv:1803.09655, 2018.
29. 29. Cubuk, E.D., et al. Autoaugment: Learning augmentation strategies from data. in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019.
30. 30. Çelik, M. and Ö. İnik, Detection of monkeypox among different pox diseases with different pre-trained deep learning models. Journal of the Institute of Science and Technology. 13(1): p. 10-21.
31. 31. Ali, M.S., et al., An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models. Machine Learning with Applications, 2021. 5: p. 100036.
32. 32. Chanda, D., et al., DCENSnet: A new deep convolutional ensemble network for skin cancer classification. Biomedical Signal Processing and Control, 2024. 89: p. 105757.
33. 33. Albahar, M.A., Skin lesion classification using convolutional neural network with novel regularizer. IEEE Access, 2019. 7: p. 38306-38313.
34. 34. Sanketh, R.S., et al. Melanoma disease detection using convolutional neural networks. in 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS). 2020. IEEE.
35. 35. Daghrir, J., et al. Melanoma skin cancer detection using deep learning and classical machine learning techniques: A hybrid approach. in 2020 5th international conference on advanced technologies for signal and image processing (ATSIP). 2020. IEEE.
36. 36. Vipin, V., et al. Detection of melanoma using deep learning techniques: A review. in 2021 international conference on communication, control and information sciences (ICCISc). 2021. IEEE.
37. 37. Rahi, M.M.I., et al. Detection of skin cancer using deep neural networks. in 2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE). 2019. IEEE.
38. 38. Jojoa Acosta, M.F., et al., Melanoma diagnosis using deep learning techniques on dermatoscopic images. BMC Medical Imaging, 2021. 21: p. 1-11.
39. 39. Yu, L., et al., Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE transactions on medical imaging, 2016. 36(4): p. 994-1004.
40. 40. Majtner, T., S. Yildirim-Yayilgan, and J.Y. Hardeberg. Combining deep learning and hand-crafted features for skin lesion classification. in 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA). 2016. IEEE.
41. 41. Li, Y. and L. Shen, Skin lesion analysis towards melanoma detection using deep learning network. Sensors, 2018. 18(2): p. 556.
42. 42. Mahbod, A., et al., Fusing fine-tuned deep features for skin lesion classification. Computerized Medical Imaging and Graphics, 2019. 71: p. 19-29.
43. 43. Zhang, J., et al., Attention residual learning for skin lesion classification. IEEE transactions on medical imaging, 2019. 38(9): p. 2092-2103.
44. 44. Amin, J., et al., Integrated design of deep features fusion for localization and classification of skin cancer. Pattern Recognition Letters, 2020. 131: p. 63-70.
45. 45. Mahbod, A., et al., Transfer learning using a multi-scale and multi-network ensemble for skin lesion classification. Computer methods and programs in biomedicine, 2020. 193: p. 105475.
46. 46. Kwasigroch, A., M. Grochowski, and A. Mikołajczyk, Neural architecture search for skin lesion classification. IEEE Access, 2020. 8: p. 9061-9071.
47. 47. Hameed, N., et al., Multi-class multi-level classification algorithm for skin lesions classification using machine learning techniques. Expert Systems with Applications, 2020. 141: p. 112961.
48. 48. Khan, M.A., et al., Developed Newton-Raphson based deep features selection framework for skin lesion recognition. Pattern Recognition Letters, 2020. 129: p. 293-303.
49. 49. Mporas, I., I. Perikos, and M. Paraskevas. Color models for skin lesion classification from dermatoscopic images. in Advances in Integrations of Intelligent Methods: Post-workshop volume of the 8th International Workshop CIMA 2018, Volos, Greece, November 2018 (in conjunction with IEEE ICTAI 2018). 2020. Springer.
50. 50. Khan, M.A., et al., Pixels to classes: intelligent learning framework for multiclass skin lesion localization and classification. Computers & Electrical Engineering, 2021. 90: p. 106956.
51. 51. Pereira, P.M., et al., Skin lesion classification enhancement using border-line features–The melanoma vs nevus problem. Biomedical Signal Processing and Control, 2020. 57: p. 101765.
52. 52. Khan, M.A., et al., Skin lesion segmentation and multiclass classification using deep learning features and improved moth flame optimization. Diagnostics, 2021. 11(5): p. 811.