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Melanoma Classification Using Log-Gabor Filter and Ensemble of Deep Convolution Neural Networks

  • Long, Hoang (Department of Artificial Intelligence Convergence, Pukyong National University) ;
  • Lee, Suk-Hwan (Dept. of Computer Engineering, Donga University) ;
  • Kwon, Seong-Geun (Department of Electronics Engineering, Kyungil University) ;
  • Kwon, Ki-Ryong (Department of Artificial Intelligence Convergence, Pukyong National University)
  • Received : 2022.07.21
  • Accepted : 2022.07.27
  • Published : 2022.08.31
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Abstract

Melanoma is a skin cancer that starts in pigment-producing cells (melanocytes). The death rates of skin cancer like melanoma can be reduced by early detection and diagnosis of diseases. It is common for doctors to spend a lot of time trying to distinguish between skin lesions and healthy cells because of their striking similarities. The detection of melanoma lesions can be made easier for doctors with the help of an automated classification system that uses deep learning. This study presents a new approach for melanoma classification based on an ensemble of deep convolution neural networks and a Log-Gabor filter. First, we create the Log-Gabor representation of the original image. Then, we input the Log-Gabor representation into a new ensemble of deep convolution neural networks. We evaluated the proposed method on the melanoma dataset collected at Yonsei University and Dongsan Clinic. Based on our numerical results, the proposed framework achieves more accuracy than other approaches.

Keywords

Acknowledgement

This research was supported by by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2022-2020-0-01797) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation) and the MSIT(Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program(IITP-2022-2016-0-00318) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation)"

References

  1. C.C. Darmawan, G. Jo, S.E. Montenegro, Y. Kwak, L. Cheol, K.H. Cho, and J.-H. Mun, "Early Detection of Acral Melanoma: A Review of Clinical, Dermoscopic, Histopathologic, and Molecular Characteristics," Journal of the American Academy of Dermatology, Vol. 81, No. 3, pp. 805-812, 2019. https://doi.org/10.1016/j.jaad.2019.01.081
  2. R. Raza, F. Zulfiqar, S. Tariq, G.B. Anwar, A.B. Sargano, and Z. Habib, "Melanoma Classification from Dermoscopy Images Using Ensemble of Convolutional Neural Networks," Mathematics, Vol. 10, No. 1, p. 26, 2021.
  3. L. Rey-Barroso, S. Pena-Gutierrez, C. Yanez, F.J. Burgos-Fernandez, M. Vilaseca, and S. Royo, "Optical Technologies for the Improvement of Skin Cancer Diagnosis: A Review," Sensors, Vol. 21, No. 1, p. 252, 2021.
  4. K.M. Hosny, M.A. Kassem, and M.M. Foaud, "Classification of Skin Lesions Using Transfer Learning and Augmentation with Alex-Net," PLOS ONE, Vol. 14, No. 5, p. e0217293, 2019.
  5. World Health Organization, Radiation: Ultraviolet (UV) Radiation and Skin Cancer. Available online: https://www.who.int/news-room/questions-and-answers/item/radiation-ultraviolet-(uv)-radiation-and-skin-cancer#:~:text=Currently%2C%between%2%and%3,skin%cancer%in%their%lifetime (accessed June 28, 2022).
  6. J. Trufant and E. Jones, "Skin Cancer for Primary Care," Common Dermatologic Conditions in Primary Care, pp. 171-208, 2019.
  7. Q. Abbas, F. Ramzan, and M.U. Ghani, "Acral Melanoma Detection Using Dermoscopic Images and Convolutional Neural Networks," Visual Computing for Industry, Biomedicine, and Art, Vol. 4, No. 1, pp. 1-12, 2021. https://doi.org/10.1186/s42492-020-00067-5
  8. A. Antony, A. Ramesh, A. Sojan, B. Mathews, and T.A. Varghese, "Skin Cancer Detection Using Artificial Neural Networking," International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, Vol. 4, No. 4, e-ISSN: 2321-2004, 2016.
  9. M.S. Poornima and K. Shailaja, "Detection of Skin Cancer Using SVM," International Research Journal of Engineering and Technology, Vol. 4, No. 7, e-ISSN: 2395-0056, 2017.
  10. H. Alquran, I.A. Qasmieh, A.M. Alqudah, S. Alhammouri, E. Alawneh, A. Abughazaleh, and F. Hasayen, "The Melanoma Skin Cancer Detection and Classification Using Support Vector Machine," Proceedings of the 2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), pp. 1-5, 2017.
  11. C. Yu, S. Yang, W. Kim, J. Jung, K.-Y. Chung, S.W. Lee, and B. Oh, "Acral Melanoma Detection Using a Convolutional Neural Network for Dermoscopy Images," PLOS ONE, Vol. 13, No. 3, p. e0193321, 2018.
  12. S.O. Oppong, F. Twum, J.B. Hayfron-Acquah, and Y.M. Missah, "Medicinal Plant Identification Using Gabor Filters and Deep Learning Techniques: A Paper Review," Journal of Computer Science, Vol. 17, No. 12, pp. 1210-1221, 2021. https://doi.org/10.3844/jcssp.2021.1210.1221
  13. R. Malviya, R. Kumar, A. Dangi, and P. Kumawat, "Verification of Palm Print Using Log Gabor Filter and Comparison with ICA," International Journal of Computer Applications in Engineering Sciences, Vol. 1, pp. 222-227, 2011.
  14. O. Sharifi and M. Eskandari, "Optimal Face-Iris Multimodal Fusion Scheme," Symmetry, Vol. 8, No. 6, p. 48, 2016.
  15. F. Zhu, C. Liu, J. Yang, and S. Wang, "An Improved MobileNet Network with Wavelet Energy and Global Average Pooling for Rotating Machinery Fault Diagnosis," Sensors, Vol. 22, No. 12, p. 4427, 2022.
  16. T.W. Teng, P. Veerajagadheswar, B. Ramalingam, J. Yin, R. Elara Mohan, and B.F. Gomez, "Vision based Wall Following Framework: A Case Study with HSR Robot for Cleaning Application," Sensors, Vol. 20, No. 11, p. 3298, 2020.
  17. T. Lawrence and L. Zhang, "IoTNet: An Efficient and Accurate Convolutional Neural Network for IOT Devices," Sensors, Vol. 19, No. 24, p. 5541, 2019.
  18. Y. Lee and J. Lee, "A Novel Feature Selection Method for Output Coding Based Multiclass SVM," Journal of Korea Multimedia Society, Vol. 16, No. 7, pp. 795-801, 2013. https://doi.org/10.9717/KMMS.2013.16.7.795
  19. M. Senekane, "Differentially Private Image Classification Using Support Vector Machine and Differential Privacy," Machine Learning and Knowledge Extraction, Vol. 1, No. 1, pp. 483-491, 2019. https://doi.org/10.3390/make1010029
  20. Acral Melanoma and Benign Data Set, Available online: https://figshare.com/s/a8c22c09f999f60a81bd (accessed June 28, 2022).
  21. R. Raza, F. Zulfiqar, S. Tariq, G.B. Anwar, A.B. Sargano, and Z. Habib, "Melanoma Classification from Dermoscopy Images Using Ensemble of Convolutional Neural Networks," Mathematics, Vol. 10, No. 1, p. 26, 2021.
  22. J.A. Salido and C. Rulz, "Using Deep Learning for Melanoma Detection in Dermoscopy Images," International Journal of Machine Learning and Computing, Vol. 8, No. 1, pp. 61-68, 2018. https://doi.org/10.18178/ijmlc.2018.8.1.664
  23. S. Lee, Y.S. Chu, S.K. Yoo, S. Choi, S.J. Choe, S.B. Koh, K.Y. Chung, L. Xing, B. Oh, and S. Yang, "Augmented Decision-Making for Acral Lentiginous Melanoma Detection Using Deep Convolutional Neural Networks," Journal of the European Academy of Dermatology and Venereology, Vol. 34, No. 8, pp. 1842-1850, 2020. https://doi.org/10.1111/jdv.16185