Classification of Skin Disease Images Using EfficientNetTransfer Learning Technique

Year : 2024 | Volume :11 | Issue : 01 | Page : 7-12
By

B.R. Nikhitha

Shantakumar B. Patil

Poornima Gowda

Ananya M.V.

Apeksha Belavanaki

Ushashree P.

  1. Student Department of Computer Science Engineering, Sai Vidya Institute of Technology, Haddosiddapura, Bengaluru Karnataka
  2. Professor Department of Computer Science Engineering, Sai Vidya Institute of Technology, Haddosiddapura, Bengaluru Karnataka India
  3. Professor Department of Computer Science Engineering, Sai Vidya Institute of Technology, Haddosiddapura, Bengaluru Karnataka India
  4. Student Department of Computer Science Engineering, Sai Vidya Institute of Technology, Haddosiddapura, Bengaluru Karnataka India
  5. Student Department of Computer Science Engineering, Sai Vidya Institute of Technology, Haddosiddapura, Bengaluru Karnataka India
  6. Student Department of Computer Science Engineering, Sai Vidya Institute of Technology, Haddosiddapura, Bengaluru Karnataka

Abstract

Melanomous Skin lesions are among the deadliest kinds of cancer. Despite being a very uncommon skin condition, melanoma is responsible for 75% of cancer-related deaths. Dermatologists perform dermoscopy more commonly than any other procedure. Since it aggravates the skin condition, a dermatologist will be the first to identify it during an inspection. Because this strategy depends on the user’s visual perception and experience, it can only be used by highly qualified physicians. These difficulties motivate researchers to create novel methods for recognizing and categorizing skin lesions.Skin diseases present a significant public health challenge globally, necessitating efficient and accurate diagnostic methods for timely treatment. With the advent of deep learning techniques, automated classification of skin disease images has emerged as a promising approach to aid dermatologists in diagnosis. In this study, we propose a novel approach utilizing transfer learning with EfficientNet, a state-of-the-art deep learning architecture, for the classification of skin disease images.

Keywords: EfficientNet, transfer learning, skin disease, melanomous skin, CNN (Central Neural Network).

[This article belongs to Journal of Telecommunication, Switching Systems and Networks(jotssn)]

How to cite this article: B.R. Nikhitha, Shantakumar B. Patil, Poornima Gowda, Ananya M.V., Apeksha Belavanaki, Ushashree P.. Classification of Skin Disease Images Using EfficientNetTransfer Learning Technique. Journal of Telecommunication, Switching Systems and Networks. 2024; 11(01):7-12.
How to cite this URL: B.R. Nikhitha, Shantakumar B. Patil, Poornima Gowda, Ananya M.V., Apeksha Belavanaki, Ushashree P.. Classification of Skin Disease Images Using EfficientNetTransfer Learning Technique. Journal of Telecommunication, Switching Systems and Networks. 2024; 11(01):7-12. Available from: https://journals.stmjournals.com/jotssn/article=2024/view=151333

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References

  1. Litjens, Geert, et al. “A survey on deep learning in medical image analysis. “Medical image analysis42 (2017): 60-88.
  2. Russakovsky et al., “ImageNet large scale visual recognition challenge”, Int. J. Comput. Vis., vol. 115, no. 3, pp. 211-252, Dec. 2015
  3. Ker, Justin, et al. “Deep learning applications in medical image analysis.” IEEE Access6 (2017): 9375-9389.
  4. LeCun, L. Bottou, Y. Bengio and P. Haffner, “Gradient-based learning applied to document recognition”, Proc. IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998
  5. Girshick, J. Donahue, T. Darrell and J. Malik, “Region-based convolutional networks for accurate object detection and semantic segmentation”, IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 1, pp. 142-158, Jan. 2016
  6. Shin, Hoo-Chang, et al. “Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. “IEEE transactions on medical imaging35.5 (2016): 1285-1298
  7. LeCun, B. Boser, J.S. Denker, D. Henderson, R.E. Howard, W. Hubbard, L.D. Jackel. Backpropagation Applied to Handwritten Zip Code Recognition. Neural Computation, 1989
  8. Roth. High-order markov random fields for low-level vision. PhD Thesis, Brown Univ., 2007.
  9. Jain, Viren, and Sebastian Seung. “Natural image denoising with convolutional networks.” Advances in neural information processing systems21 (2008).
  10. Enkhsaikhan, W. Liu, E.-J. Holden, and P. During, Auto-labelling entities in low-resource text: A geological case study, Knowl. Inf. Syst., vol. 63, no. 3, pp. 695–715, Mar. 2021.
  11. Fang, H. Luo, S. Xu, P. E. D. Love, Z. Lu, and C. Ye, Automated text classification of near-misses from safety reports: An improved deep learning approach, Adv. Eng. Informat., vol. 44, Apr. 2020, Art. no. 101060.
  12. Gautam, Transfer learning for COVID-19 cases and deaths forecast using LSTM network, ISA Trans., vol. 124, pp. 41–56, May 2022. [9] Kaggle. Accessed: Apr. 11, 2022. [Online]. Available: https://www.kaggle.com/datasets/bittlingmayer/amazonreviews
Regular Issue Subscription Original Research
Volume 11
Issue 01
Received May 10, 2024
Accepted May 20, 2024
Published June 2, 2024
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