Multi-Input Melanoma Classification Using Mobilenet-V3-Large Architecture
By:
References
- R. L. Siegel, K. D. Miller, H. E. Fuchs, and A. Jemal, “Cancer statistics, 2022,” CAA Cancer J Clinicians, vol. 72, no. 1, pp. 7–33, Jan. 2022, doi: 10.3322/caac.21708.
- R. L. Siegel, K. D. Miller, N. S. Wagle, and A. Jemal, “Cancer statistics, 2023,” CAA Cancer J Clinicians, vol. 73, no. 1, pp. 17–48, Jan. 2023, doi: 10.3322/caac.21763.
- S. Aksoy, P. Demircioglu, and I. Bogrekci, “Enhancing Melanoma Diagnosis with Advanced Deep Learning Models Focusing on Vision Transformer, Swin Transformer, and ConvNeXt,” Dermatopathology, vol. 11, no. 3, pp. 239–252, Aug. 2024, doi: 10.3390/dermatopathology11030026.
- A. K. Nambisan et al., “Improving Automatic Melanoma Diagnosis Using Deep Learning-Based Segmentation of Irregular Networks,” Cancers, vol. 15, no. 4, p. 1259, Feb. 2023, doi: 10.3390/cancers15041259.
- R. Ashraf et al., “Region-of-Interest Based Transfer Learning Assisted Framework for Skin Cancer Detection,” IEEE Access, vol. 8, pp. 147858–147871, 2020, doi: 10.1109/ACCESS.2020.3014 701.
- M. Elgamal, “Automatic Skin Cancer Images Classification,” IJACSA, vol. 4, no. 3, 2013, doi: 10.145 69/IJACSA.2013.040342.
- F. Xiang, R. Lucas, S. Hales, and R. Neale, “Incidence of Nonmelanoma Skin Cancer in Relation to Ambient UV Radiation in White Populations, 1978-2012: Empirical Relationships,” JAMA Dermatol, vol. 150, no. 10, p. 1063, Oct. 2014, doi: 10.1001/jamadermatol.2014.762.
- A. Stratigos et al., “Diagnosis and treatment of invasive squamous cell carcinoma of the skin: European consensus-based interdisciplinary guideline,” European Journal of Cancer, vol. 51, no. 14, pp. 1989–2007, Sep. 2015, doi: 10.1016/j.ejca.2015.06.110.
- D. Koh, H. Wang, J. Lee, K. S. Chia, H. P. Lee, and C. L. Goh, “Basal cell carcinoma, squamous cell carcinoma and melanoma of the skin: analysis of the Singapore Cancer Registry data 1968-97,” Br J Dermatol, vol. 148, no. 6, pp. 1161–1166, Jun. 2003, doi: 10.1046/j.1365-2133.2003.05223.x.
- L. Fania et al., “Basal Cell Carcinoma: From Pathophysiology to Novel Therapeutic Approaches,” Biomedicines, vol. 8, no. 11, p. 449, Oct. 2020, doi: 10.3390/biomedicines8110449.
- L. Fania et al., “Cutaneous Squamous Cell Carcinoma: From Pathophysiology to Novel Therapeutic Approaches,” Biomedicines, vol. 9, no. 2, p. 171, Feb. 2021, doi: 10.3390/biomedicines9020 171.
- S. K. T. Que, F. O. Zwald, and C. D. Schmults, “Cutaneous squamous cell carcinoma,” Journal of the American Academy of Dermatology, vol. 78, no. 2, pp. 237–247, Feb. 2018, doi: 10.1016/j.ja ad.2017.08.059.
- M. Rubatto et al., “Classic and new strategies for the treatment of advanced melanoma and nonmelanoma skin cancer,” Front. Med., vol. 9, p. 959289, Feb. 2023, doi: 10.3389/fmed.2022.95 9289.
- S. R. Waheed, N. M. Suaib, M. S. Mohd Rahim, M. Mundher Adnan, and A. A. Salim, “Deep Learning Algorithms-based Object Detection and Localization Revisited,” J. Phys.: Conf. Ser., vol. 1892, no. 1, p. 012001, Apr. 2021, doi: 10.1088/1742-6596/1892/1/012001.
- S. R. Waheed et al., “Melanoma Skin Cancer Classification based on CNN Deep Learning Algorithms,” Mal. J. Fund. Appl. Sci., vol. 19, no. 3, pp. 299–305, May 2023, doi: 10.11113/mjfas.v19n3.2900.
- F. Pereira Dos Santos and M. Antonelli Ponti, “Robust Feature Spaces from Pre-Trained Deep Network Layers for Skin Lesion Classification,” in 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Parana: IEEE, Oct. 2018, pp. 189–196. doi: 10.1109/SIBGRA PI.2018.00031.
- P. Tschandl, C. Rosendahl, and H. Kittler, “The HAM10000 dataset, a large collection of multisource dermatoscopic images of common pigmented skin lesions,” Sci Data, vol. 5, no. 1, p. 180161, Aug. 2018, doi: 10.1038/sdata.2018.161.
- D. Gutman et al., “Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC),” 2016, doi: 10.48550/ARXIV.1605.01397.
- N. Codella et al., “Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC),” 2019, doi: 10.48550/ARXIV.1902.03368.
- B. Cassidy, C. Kendrick, A. Brodzicki, J. JaworekKorjakowska, and M. H. Yap, “Analysis of the ISIC image datasets: Usage, benchmarks and recommendations,” Medical Image Analysis, vol. 75, p. 102305, Jan. 2022, doi: 10.1016/j.media.2021.102305.
- M. Combalia et al., “BCN20000: Dermoscopic Lesions in the Wild,” 2019, doi: 10.48550/ARX IV.1908.02288
- 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, p. 100034, Dec. 2022, doi: 10.1016/j.neuri.2021.100034.
- K. H. Cheong et al., “An automated skin melanoma detection system with melanomaindex based on entropy features,” Biocybernetics and Biomedical Engineering, vol. 41, no. 3, pp. 997–1012, Jul. 2021, doi: 10.1016/j.bbe.2021.05.010.
- 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, Dec. 2017, doi: 10.1016/j.eswa.2017.08.010.
- Q. Ul Ain, Bing Xue, H. Al-Sahaf, and M. Zhang, “Genetic programming for skin cancer detection in dermoscopic images,” in 2017 IEEE Congress on Evolutionary Computation (CEC), Donostia, San Sebastián, Spain: IEEE, Jun. 2017, pp. 2420–2427. doi: 10.1109/CEC.2017.7969598.
- R. Garg, S. Maheshwari, and A. Shukla, “Decision Support System for Detection and Classification of Skin Cancer using CNN,” 2019, doi: 10.48550/ARXIV.1912.03798.
- T. H. H. Aldhyani, A. Verma, M. H. Al-Adhaileh, and D. Koundal, “Multi-Class Skin Lesion Classification Using a Lightweight Dynamic Kernel DeepLearning-Based Convolutional Neural Network,” Diagnostics, vol. 12, no. 9, p. 2048, Aug. 2022, doi: 10.3390/diagnostics12092048.
- S. K. Datta, M. A. Shaikh, S. N. Srihari, and M. Gao, “Soft-Attention Improves Skin Cancer Classification Performance,” 2021, doi: 10.48550/ARXIV.2105.03358.
- M. Abd Elaziz, A. Dahou, A. Mabrouk, S. ElSappagh, and A. O. Aseeri, “An Efficient Artificial Rabbits Optimization Based on Mutation Strategy For Skin Cancer Prediction,” Computers in Biology and Medicine, vol. 163, p. 107154, Sep. 2023, doi: 10.1016/j.compbiomed.2023.10 7154.
- C. Xin et al., “An improved transformer network for skin cancer classification,” Computers in Biology and Medicine, vol. 149, p. 105939, Oct. 2022, doi: 10.1016/j.compbiomed.2022.105939.
- S. Gupta, N. Sharma, R. Tyagi, P. Singh, A. Aggarwal, and S. Chawla, “Cognitive Inspired & Computationally-Intelligent Early Melanoma Detection Using Feature Analysis Techniques,” JAIT, Oct. 2023, doi: 10.37965/jait.2023.0334.
- K. Kachare, N. Bhagat, and P. Raundale, “Advancements in Melanoma Skin Cancer Detection Using Deep Learning: A Comprehensive Review,” in 2023 7th International Conference On Computing, Communication, Control And Automation (ICCUBEA), Pune, India: IEEE, Aug. 2023, pp. 1–6. doi: 10.1109/ICCU BEA58933.2023.10392174.
- K. Tomar, U. Panwar, and R. Gupta, “A melanoma skin cancer detection and analysis using dermoscopy image processing,” presented at the MACHINE LEARNING AND INFORMATION PROCESSING: PROCEEDINGS OF ICMLIP 2023, Ranchi, India, 2023, p. 080007. doi: 10.1063/5.0168906.
- R. Mittal, F. Jeribi, R. J. Martin, V. Malik, S. J. Menachery, and J. Singh, “DermCDSM: Clinical Decision Support Model for Dermatosis Using Systematic Approaches of Machine Learning and Deep Learning,” IEEE Access, vol. 12, pp. 47319–47337, 2024, doi: 10.1109/ACCESS.2024.3373 539.
- S. Aksoy, P. Demircioglu, and I. Bogrekci, “Advanced Artificial Intelligence Techniques for Comprehensive Dermatological Image Analysis and Diagnosis,” Dermato 2024, vol. 4, no. 4, pp. 173–186, doi: 10.3390/dermato4040015.
Language: English
Page range: 73 - 84
Submitted on: May 24, 2024
Accepted on: Jul 9, 2024
Published on: Mar 31, 2025
Published by: Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year
Keywords:
Related subjects:
© 2025 Serra Aksoy, published by Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.