Melanoma Skin Cancer Classification Using EfficientNetB7 for Deep Feature Extraction and Ensemble Learning Approach
Abstract
Cancer is one of the deadliest diseases in the world. cancer is caused by the presence of cancer cells due to abnormal conditions during the cell turnover process. One of the dangerous types of cancer is melanoma skin cancer, this cancer attacks the outer skin of humans because skin cells are prone to damage. However, diagnosis for this disease is mostly done manually while there are previous studies that use deep learning approaches with the accuracy that can be improved. The purpose of this study is to find an effective and efficient method for melanoma cancer recognition so that it can be treated more quickly. We propose several methods that we have compared to be able to classify melanoma skin cancer with EfficientNetB7 Feature Extractor and Ensemble Learning. The results of this research model get the highest accuracy of 91.2%. When EfficientNetB7 together with ensemble learning. This research model has better and efficient results when compared to previous research.
Full Text:
PDF (43-50)References
O. H. Kesav and R. G. K, “A Systematic Study on Enhanced Deep Learning Based Methodologies for Detection and Classification of Early Stage Cancers,” in 2023 IEEE 5th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), IEEE, Oct. 2023, pp. 328–333. doi: 10.1109/ICCCMLA58983.2023.10346973.
G. Zhao et al., “Nanomaterials in crossroad of autophagy control in human cancers: Amplification of cell death mechanisms,” Cancer Lett, vol. 591, p. 216860, Jun. 2024, doi: 10.1016/j.canlet.2024.216860.
S. Haggenmüller et al., “Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts,” Eur J Cancer, vol. 156, pp. 202–216, Oct. 2021, doi: 10.1016/j.ejca.2021.06.049.
R. N. P. Fauziyyah, M. Komariah, and Y. K. Herliani, “Sunlight Exposure and Protection Behavior as Prevention of Skin Cancer in Nursing Students,” Indonesian Journal of Cancer, vol. 17, no. 1, p. 1, Mar. 2023, doi: 10.33371/ijoc.v17i1.921.
E. R. Parker, “The influence of climate change on skin cancer incidence – A review of the evidence,” Int J Womens Dermatol, vol. 7, no. 1, pp. 17–27, Jan. 2021, doi: 10.1016/j.ijwd.2020.07.003.
A. Shah et al., “A comprehensive study on skin cancer detection using artificial neural network (ANN) and convolutional neural network (CNN),” Clinical eHealth, vol. 6, pp. 76–84, Dec. 2023, doi: 10.1016/j.ceh.2023.08.002.
S. Andromeda and N. L. B. Dwijaksara, “AI in Dermatology: A Systematic Review on Skin Cancer Detection,” TIERS Information Technology Journal, vol. 5, no. 1, pp. 41–51, Jun. 2024, doi: 10.38043/tiers.v5i1.5444.
V. Singh, K. A. Sultanpure, and H. Patil, “Frontier machine learning techniques for melanoma skin cancer identification and categorization: An in-Depth review,” Oral Oncology Reports, vol. 9, p. 100217, Mar. 2024, doi: 10.1016/j.oor.2024.100217.
S. Pandey and G. K. Shankhdhar, “Skin cancer diagnosis using the deep learning advancements: a technical review,” Bulletin of Electrical Engineering and Informatics, vol. 13, no. 3, pp. 1847–1856, Jun. 2024, doi: 10.11591/eei.v13i3.5925.
V. Venugopal, N. I. Raj, M. K. Nath, and N. Stephen, “A deep neural network using modified EfficientNet for skin cancer detection in dermoscopic images,” Decision Analytics Journal, vol. 8, p. 100278, Sep. 2023, doi: 10.1016/j.dajour.2023.100278.
P. Tang, Q. Liang, X. Yan, S. Xiang, and D. Zhang, “GP-CNN-DTEL: Global-Part CNN Model With Data-Transformed Ensemble Learning for Skin Lesion Classification,” IEEE J Biomed Health Inform, vol. 24, no. 10, pp. 2870–2882, Oct. 2020, doi: 10.1109/JBHI.2020.2977013.
A. Mayya et al., “Efficient hybrid ensembles of CNNs and transfer learning models for bridge deck image-based crack detection,” Structures, vol. 64, p. 106538, Jun. 2024, doi: 10.1016/j.istruc.2024.106538.
T. T. Khuat, R. Bassett, E. Otte, A. Grevis-James, and B. Gabrys, “Applications of machine learning in antibody discovery, process development, manufacturing and formulation: Current trends, challenges, and opportunities,” Comput Chem Eng, vol. 182, p. 108585, Mar. 2024, doi: 10.1016/j.compchemeng.2024.108585.
D. Keerthana, V. Venugopal, M. K. Nath, and M. Mishra, “Hybrid convolutional neural networks with SVM classifier for classification of skin cancer,” Biomedical Engineering Advances, vol. 5, p. 100069, Jun. 2023, doi: 10.1016/j.bea.2022.100069.
P. Bansal, K. Gehlot, A. Singhal, and A. Gupta, “Automatic detection of osteosarcoma based on integrated features and feature selection using binary arithmetic optimization algorithm,” Multimed Tools Appl, vol. 81, no. 6, pp. 8807–8834, Mar. 2022, doi: 10.1007/s11042-022-11949-6.
M. Azim Mim, N. Majadi, and P. Mazumder, “A soft voting ensemble learning approach for credit card fraud detection,” Heliyon, vol. 10, no. 3, p. e25466, Feb. 2024, doi: 10.1016/j.heliyon.2024.e25466.
S. Viswanathan, N. V. Sridharan, J. Rakkiyannan, and S. Vaithiyanathan, “Brake fault diagnosis using a voting ensemble of machine learning classifiers,” Results in Engineering, vol. 23, p. 102857, Sep. 2024, doi: 10.1016/j.rineng.2024.102857.
S. Inthiyaz et al., “Skin disease detection using deep learning,” Advances in Engineering Software, vol. 175, p. 103361, Jan. 2023, doi: 10.1016/j.advengsoft.2022.103361.
I. A. Kandhro et al., “Performance evaluation of E-VGG19 model: Enhancing real-time skin cancer detection and classification,” Heliyon, vol. 10, no. 10, p. e31488, May 2024, doi: 10.1016/j.heliyon.2024.e31488.
Muhammad Hasnain Javid, “Melanoma Skin Cancer Dataset of 10000 Images,” Kaggle.
N. yahia Ibrahim and A. S. Talaat, “An Enhancement Technique to Diagnose Colon and Lung Cancer by using Double CLAHE and Deep Learning,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 8, 2022, doi: 10.14569/IJACSA.2022.0130833.
H. Syamsudin, S. Khalidah, and J. Unjung, “Lepidoptera Classification Using Convolutional Neural Network EfficientNet-B0,” Indonesian Journal of Artificial Intelligence and Data Mining, vol. 7, no. 1, p. 47, Nov. 2023, doi: 10.24014/ijaidm.v7i1.24586.
T. Selim, I. Elkabani, and M. A. Abdou, “Students Engagement Level Detection in Online e-Learning Using Hybrid EfficientNetB7 Together With TCN, LSTM, and Bi-LSTM,” IEEE Access, vol. 10, pp. 99573–99583, 2022, doi: 10.1109/ACCESS.2022.3206779.
J. Hu, L. Shen, S. Albanie, G. Sun, and E. Wu, “Squeeze-and-Excitation Networks,” Sep. 2017, [Online]. Available: http://arxiv.org/abs/1709.01507
G. Obaido et al., “Supervised machine learning in drug discovery and development: Algorithms, applications, challenges, and prospects,” Machine Learning with Applications, vol. 17, p. 100576, Sep. 2024, doi: 10.1016/j.mlwa.2024.100576.
M. Niazkar et al., “Applications of XGBoost in water resources engineering: A systematic literature review (Dec 2018–May 2023),” Environmental Modelling & Software, vol. 174, p. 105971, Mar. 2024, doi: 10.1016/j.envsoft.2024.105971.
H. Qiu, Y. Xia, C. Xiang, F. Xu, L. Sun, and Y. Zou, “Prediction of hydrogen storage in metal-organic frameworks using CatBoost-based approach,” Int J Hydrogen Energy, vol. 79, pp. 952–961, Aug. 2024, doi: 10.1016/j.ijhydene.2024.07.078.
Y. Wu, “From ensemble learning to deep ensemble learning: A case study on multi-indicator prediction of pavement performance,” Appl Soft Comput, vol. 166, p. 112188, Nov. 2024, doi: 10.1016/j.asoc.2024.112188.
X. Yin, H. Tian, F. Zhang, C. Xu, L. Tang, and Y. Wei, “Identification of millet origin using terahertz spectroscopy combined with ensemble learning,” Infrared Phys Technol, vol. 142, p. 105547, Nov. 2024, doi: 10.1016/j.infrared.2024.105547.
R. Rofik, R. Aulia, K. Musaadah, S. S. F. Ardyani, and A. A. Hakim, “Optimization of Credit Scoring Model Using Stacking Ensemble Learning and Oversampling Techniques,” Journal of Information System Exploration and Research, vol. 2, no. 1, Dec. 2023, doi: 10.52465/joiser.v2i1.203.
E. Dönmez, A. Diker, A. Elen, and M. Ulu, “Multiple deep learning by majority-vote to classify haploid and diploid maize seeds,” Sci Hortic, vol. 337, p. 113549, Nov. 2024, doi: 10.1016/j.scienta.2024.113549.
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 (Switzerland), vol. 10, no. 7, Jul. 2022, doi: 10.3390/healthcare10071183.
A. Javaid, M. Sadiq, and F. Akram, “Skin Cancer Classification Using Image Processing and Machine Learning,” in 2021 International Bhurban Conference on Applied Sciences and Technologies (IBCAST), IEEE, Jan. 2021, pp. 439–444. doi: 10.1109/IBCAST51254.2021.9393198.
Refbacks
- There are currently no refbacks.