Wn-Based Skin Cancer Lesion Segmentation of Melanoma Using Deep Learning Methods
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References
R. Sadri, S. Azarianpour, M. Zekri, M. Emre Celebi, and S. Sadri, “WN-based approach to melanoma diagnosis from dermoscopy images,” IET Image Processing, vol. 11, no. 7, pp. 475–482, 2017, doi: https://doi.org/10.1049/ietipr.2016.0681
M. A. Albahar, “Skin Lesion Classification Using Convolutional Neural Network With Novel Regularizer,” IEEE Access, vol. 7, pp. 38306–38313, 2019, doi: https://doi.org/10.1109/access.2019.2906241
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, p. 101756, 2019, doi: https://doi.org/10.1016/j.artmed.2019.101756
Y. Guo and A. S. Ashour, “11 - Neutrosophic sets in dermoscopic medical image segmentation,” ScienceDirect, Jan. 01, 2019. Available: https://doi.org/10.1016/B978-0-12-818148-5.00011-4
T. Sreelatha, M. V. Subramanyam, and M. N. G. Prasad, “Early Detection of Skin Cancer Using Melanoma Segmentation technique,” Journal of Medical Systems, vol. 43, no. 7, 2019, doi: https://doi.org/10.1007/s10916-019- 1334-1
M. A. Kadampur and S. Al Riyaee, “Skin cancer detection: Applying a deep learning based model driven architecture in the cloud for classifying dermal cell images,” Informatics in Medicine Unlocked, vol. 18, p. 100282, 2020, doi: https://doi.org/10.1016/j.imu.2019.100282
F. Navarro, M. Escudero-Viñolo, and J. Bescós, “Accurate Segmentation and Registration of Skin Lesion Images to Evaluate Lesion Change,” IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 2, pp. 501–508, 2019, doi: https://doi.org/10.1109/JBHI.2018.2825251
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, 2020, doi: https://doi.org/10.1016/j.matpr.2020.07.366
T. G. Debelee, “Skin Lesion Classification and Detection Using Machine Learning Techniques: A Systematic Review,” Diagnostics (Basel, Switzerland), vol. 13, no. 19, p. 3147, 2023, doi: https://doi.org/10.3390/diagnostics13193147
J. P. Jeyakumar, A. Jude, A. G. Priya Henry, and J. Hemanth, “Comparative Analysis of Melanoma Classification Using Deep Learning Techniques on Dermoscopy Images,” Electronics, vol. 11, no. 18, p. 2918, 2022, doi: https://doi.org/10.3390/electronics11182918
H. R, G. Glan. Devadhas, and T. Y. Satheesha, “AI Based Automated Skin lesion Classification for Melanoma Detection Using Deep Learning Techniques,” 2024 5th IEEE Global Conference for Advancement in Technology (GCAT), pp. 1–5, 2024, doi: https://doi.org/10.1109/gcat62922.2024.10923859
T. Ramesh, L. Vigneash, Samraj S, J. Shalom, Maheshwari B, and S. Kamatchi, “A Comprehensive Evaluation of Deep Learning based Melanoma Detection and Classification Scheme,” 2024 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), pp. 1–6, 2024, doi: https://doi.org/10.1109/iitcee59897.2024.10467850
M. A. Riyadi, A. Ayuningtias, and R Rizal Isnanto, “Detection and Classification of Skin Cancer Using YOLOv8n,” 2024 11th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), pp. 9– 15, 2024, doi: https://doi.org/10.1109/eecsi63442.2024.10776505
A. Dascalu and E. O. David, “Skin cancer detection by deep learning and sound analysis algorithms: A prospective clinical study of an elementary dermoscope,” EBioMedicine, vol. 43, pp. 107–113, 2019, doi: https://doi.org/10.1016/j.ebiom.2019.04.055
H. Zanddizari, N. Nguyen, B. Zeinali, and J. M. Chang, “A new preprocessing approach to improve the performance of CNN-based skin lesion classification,” Medical & Biological Engineering & Computing, vol. 59, no. 5, pp. 1123– 1131, 2021, doi: https://doi.org/10.1007/s11517-021-02355-5
Harangi, “Skin lesion classification with ensembles of deep convolutional neural networks,” Journal of Biomedical Informatics, vol. 86, pp. 25–32, Oct. 2018, doi: https://doi.org/10.1016/j.jbi.2018.08.006
A. Mahbod, G. Schaefer, I. Ellinger, R. Ecker, A. Pitiot, and C. Wang, “Fusing fine-tuned deep features for skin lesion classification,” Computerized Medical Imaging and Graphics, vol. 71, pp. 19–29, 2019, doi: https://doi.org/10.1016/j.compmedimag.2018.10.007
T. Y. Tan, L. Zhang, and C. P. Lim, “Intelligent skin cancer diagnosis using improved particle swarm optimization and deep learning models,” Applied Soft Computing, vol. 84, p. 105725, 2019, doi: https://doi.org/10.1016/j.asoc.2019.105725
T. Y. Tan, L. Zhang, S. C. Neoh, and C. P. Lim, “Intelligent skin cancer detection using enhanced particle swarm optimization,” Knowledge-Based Systems, vol. 158, pp. 118–135, Oct. 2018, doi: https://doi.org/10.1016/j.knosys.2018.05.042
F. Xie, J. Yang, J. Liu, Z. Jiang, Y. Zheng, and Y. Wang, “Skin lesion segmentation using high-resolution convolutional neural network,” Computer Methods and Programs in Biomedicine, vol. 186, p. 105241, 2020, doi: https://doi.org/10.1016/j.cmpb.2019.105241
N. Alfed and F. Khelifi, “Bagged textural and color features for melanoma skin cancer detection in dermoscopic and standard images,” Expert Systems with Applications, vol. 90, pp. 101–110, 2017, doi: https://doi.org/10.1016/j.eswa.2017.08.010
A. Aima and A. K. Sharma, “Predictive Approach for Melanoma Skin Cancer Detection using CNN,” SSRN Electronic Journal, 2019, doi: https://doi.org/10.2139/ssrn.3352407
P. Tschandl, C. Rosendahl, and H. Kittler, “The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions,” Scientific Data, vol. 5, no. 1, 2018, doi: https://doi.org/10.1038/sdata.2018.161
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Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
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