Enhanced Skin Cancer Classification using Deep Learning and Nature-based Feature Optimization

Authors

  • Talha Imran Department of Computer Science, COMSATS University Islamabad, Pakistan
  • Ahmed S. Alghamdi Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Saudi Arabia
  • Mohammed Saeed Alkatheiri Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Saudi Arabia
Volume: 14 | Issue: 1 | Pages: 12702-12710 | February 2024 | https://doi.org/10.48084/etasr.6604

Abstract

This paper presents a skin cancer classification model that combines a pre-trained Convolutional Neural Network (CNN) with a nature-inspired feature optimization algorithm. A custom dataset comprising both malignant and benign skin cancer microscopic illustrations is derived from the ISIC dataset of dermoscopic images. Several preprocessing steps are performed on the input pictures, such as histogram equalization, gamma correction, and white balance adjustment, to improve visibility, quality, and make color corrections. Deep feature extraction and pattern recognition are conducted on both enhanced and original dataset images using the pre-trained CNN model EfficientNetB0. As a result of fusing these features, the model can capture rich details from both dataset versions at the same time. Ant Colony Optimization (ACO), a nature-inspired feature selection algorithm is applied to perform model optimization by keeping the most relevant features and discarding the unnecessary ones. The optimized feature vector is then used with various SVM classifier kernels for the skin cancer classification task. The maximum achieved accuracy of the proposed model exceeded 98% through CB-SVM while maintaining an excellent prediction speed and reduced training time.

Keywords:

skin cancer, deep learning, CNN, Ant Colony Optimization (ACO), classification

Downloads

Download data is not yet available.

References

U. B. Ansari and T. Sarode, "Skin Cancer Detection Using Image Processing," International Research Journal of Engineering and Technology, vol. 4, no. 4, pp. 2875–2881, 2017.

S. Jain, V. jagtap, and N. Pise, "Computer Aided Melanoma Skin Cancer Detection Using Image Processing," Procedia Computer Science, vol. 48, pp. 735–740, Jan. 2015.

N. Zhang, Y.-X. Cai, Y.-Y. Wang, Y.-T. Tian, X.-L. Wang, and B. Badami, "Skin cancer diagnosis based on optimized convolutional neural network," Artificial Intelligence in Medicine, vol. 102, Jan. 2020, Art. no. 101756.

P. Dubal, S. Bhatt, C. Joglekar, and S. Patil, "Skin cancer detection and classification," in 6th International Conference on Electrical Engineering and Informatics, Langkawi, Malaysia, Nov. 2017, pp. 1–6.

E. Jana, R. Subban, and S. Saraswathi, "Research on Skin Cancer Cell Detection Using Image Processing," in International Conference on Computational Intelligence and Computing Research, Coimbatore, India, Dec. 2017, pp. 1–8.

H. Alquran et al., "The melanoma skin cancer detection and classification using support vector machine," in Jordan Conference on Applied Electrical Engineering and Computing Technologies, Aqaba, Jordan, Oct. 2017, pp. 1–5.

M. Dildar et al., "Skin Cancer Detection: A Review Using Deep Learning Techniques," International Journal of Environmental Research and Public Health, vol. 18, no. 10, Jan. 2021, Art. no. 5479.

U. Kamath, J. Liu, and J. Whitaker, Deep learning for NLP and speech recognition. New York, NY, USA: Springer, 2019.

J. Ker, L. Wang, J. Rao, and T. Lim, "Deep Learning Applications in Medical Image Analysis," IEEE Access, vol. 6, pp. 9375–9389, 2018.

Y. Cao, T. A. Geddes, J. Y. H. Yang, and P. Yang, "Ensemble deep learning in bioinformatics," Nature Machine Intelligence, vol. 2, no. 9, pp. 500–508, Sep. 2020.

M. K. Monika, N. Arun Vignesh, Ch. Usha Kumari, M. N. V. S. S. Kumar, and E. L. Lydia, "Skin cancer detection and classification using machine learning," Materials Today: Proceedings, vol. 33, pp. 4266–4270, Jan. 2020.

J. Daghrir, L. Tlig, M. Bouchouicha, and M. Sayadi, "Melanoma skin cancer detection using deep learning and classical machine learning techniques: A hybrid approach," in 5th International Conference on Advanced Technologies for Signal and Image Processing, Sousse, Tunisia, Sep. 2020, pp. 1–5.

R. Ashraf et al., "Region-of-Interest Based Transfer Learning Assisted Framework for Skin Cancer Detection," IEEE Access, vol. 8, pp. 147858–147871, 2020.

H. Nahata and S. P. Singh, "Deep Learning Solutions for Skin Cancer Detection and Diagnosis," in Machine Learning with Health Care Perspective: Machine Learning and Healthcare, V. Jain and J. M. Chatterjee, Eds. New York, NY, USA: Springer, 2020, pp. 159–182.

L. Wei, K. Ding, and H. Hu, "Automatic Skin Cancer Detection in Dermoscopy Images Based on Ensemble Lightweight Deep Learning Network," IEEE Access, vol. 8, pp. 99633–99647, 2020.

M. Nawaz et al., "Skin cancer detection from dermoscopic images using deep learning and fuzzy k-means clustering," Microscopy Research and Technique, vol. 85, no. 1, pp. 339–351, 2022.

W. Gouda, N. U. Sama, G. Al-Waakid, M. Humayun, and N. Z. Jhanjhi, "Detection of Skin Cancer Based on Skin Lesion Images Using Deep Learning," Healthcare, vol. 10, no. 7, Jul. 2022, Art. no. 1183.

I. Kousis, I. Perikos, I. Hatzilygeroudis, and M. Virvou, "Deep Learning Methods for Accurate Skin Cancer Recognition and Mobile Application," Electronics, vol. 11, no. 9, Jan. 2022, Art. no. 1294.

A. Atta, M. A. Khan, M. Asif, G. F. Issa, R. A. Said, and T. Faiz, "Classification of Skin Cancer empowered with convolutional neural network," in International Conference on Cyber Resilience, Dubai, United Arab Emirates, Oct. 2022, pp. 01–06.

N. Codella et al., "Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)." arXiv, Mar. 29, 2019.

M. A. Kassem, K. M. Hosny, and M. M. Fouad, "Skin Lesions Classification Into Eight Classes for ISIC 2019 Using Deep Convolutional Neural Network and Transfer Learning," IEEE Access, vol. 8, pp. 114822–114832, 2020.

S. S. Bagade and V. K. Shandilya, "Use of histogram equalization in image processing for image enhancement," International Journal of Software Engineering Research & Practices, vol. 1, no. 2, pp. 6–10, 2011.

S. Rahman, M. M. Rahman, M. Abdullah-Al-Wadud, G. D. Al-Quaderi, and M. Shoyaib, "An adaptive gamma correction for image enhancement," EURASIP Journal on Image and Video Processing, vol. 2016, no. 1, Oct. 2016, Art. no. 35.

H.-K. Lam, O. C. Au, and C.-W. Wong, "Automatic white balancing using adjacent channels adjustment in RGB domain," in International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763), Taipei, Taiwan, Jun. 2004, vol. 2, pp. 979-982 Vol.2.

M. Tan and Q. Le, "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks," in 36th International Conference on Machine Learning, Long Beach, CA, USA, Jun. 2019, pp. 6105–6114.

M. Dorigo, M. Birattari, and T. Stutzle, "Ant colony optimization," IEEE Computational Intelligence Magazine, vol. 1, no. 4, pp. 28–39, Aug. 2006.

K. Ali, Z. A. Shaikh, A. A. Khan, and A. A. Laghari, "Multiclass skin cancer classification using EfficientNets – a first step towards preventing skin cancer," Neuroscience Informatics, vol. 2, no. 4, Dec. 2022, Art. no. 100034.

S. S. Chaturvedi, J. V. Tembhurne, and T. Diwan, "A multi-class skin Cancer classification using deep convolutional neural networks," Multimedia Tools and Applications, vol. 79, no. 39, pp. 28477–28498, Oct. 2020.

M. S. Ali, M. S. Miah, J. Haque, M. M. Rahman, and M. K. Islam, "An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models," Machine Learning with Applications, vol. 5, Sep. 2021, Art. no. 100036.

S. K. Datta, M. A. Shaikh, S. N. Srihari, and M. Gao, "Soft Attention Improves Skin Cancer Classification Performance," in International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, Strasbourg, France, Sep. 2021, pp. 13–23.

S. S. Chaturvedi, K. Gupta, and P. S. Prasad, "Skin Lesion Analyser: An Efficient Seven-Way Multi-class Skin Cancer Classification Using MobileNet," in International Conference on Advanced Machine Learning Technologies and Applications, Cairo, Egypt, Mar. 2021, pp. 165–176.

S. Qasim Gilani, T. Syed, M. Umair, and O. Marques, "Skin Cancer Classification Using Deep Spiking Neural Network," Journal of Digital Imaging, vol. 36, no. 3, pp. 1137–1147, Jun. 2023.

Downloads

How to Cite

[1]
Imran, T., Alghamdi, A.S. and Alkatheiri, M.S. 2024. Enhanced Skin Cancer Classification using Deep Learning and Nature-based Feature Optimization. Engineering, Technology & Applied Science Research. 14, 1 (Feb. 2024), 12702–12710. DOI:https://doi.org/10.48084/etasr.6604.

Metrics

Abstract Views: 893
PDF Downloads: 774

Metrics Information

Most read articles by the same author(s)