Volume 18 • Issue 2 • PP: 276-283 • 2025
Fusion Based Learning Approach for Melanoma Skin Cancer Detection through Multi-Stage Convolutional Neural Networks
View PDF Abstract
Melanoma is one of the forms of skin cancer that affects people worldwide. Research indicates that nearly 75% of the global population has been impacted by melanoma. Early detection and treatment of melanoma significantly increase survival rates. However, detecting melanoma in its early stages can be challenging because dermatologists typically rely on visual examination and biopsy analysis, which is both time-consuming and labor-intensive. This highlights the need for automated, efficient methods to identify melanoma at earlier stages. Skin cancer is generally classified into two categories: melanoma and benign tumors. The goal of this study is to facilitate the early detection of melanoma by employing deep learning techniques, specifically convolutional neural networks (CNNs), to distinguish between melanoma and benign lesions using the ISIC dataset. The proposed model achieves an accuracy of 80.80%, outperforming previous approaches by offering faster and more accurate melanoma detection.
Keywords
References
[1] S. R. Waheed et al., "Melanoma skin cancer classification based on CNN deep learning algorithms," Malaysian Journal of Fundamental and Applied Sciences, vol. 19, no. 3, pp. 299–305, 2023.
[2] B. Sreedhar, M. Swamy B. E., and M. S. Kumar, "A comparative study of melanoma skin cancer detection in traditional and current image processing techniques," in Proc. 4th Int. Conf. I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India, 2020, pp. 654–658, doi: 10.1109/I-SMAC49090.2020.9243501.
[3] A. Aima and A. K. Sharma, "Predictive approach for melanoma skin cancer detection using CNN," in Proc. Int. Conf. Sustainable Computing in Science, Technology and Management (SUSCOM), Amity Univ. Rajasthan, Jaipur, India, 2019.
[4] M. A. Hossin, F. F. Rupom, H. R. Mahi, A. Sarker, F. Ahsan, and S. Warech, "Melanoma skin cancer detection using deep learning and advanced regularizer," in Proc. Int. Conf. Advanced Computer Science and Information Systems (ICACSIS), Depok, Indonesia, 2020, pp. 89–94, doi: 10.1109/ICACSIS51025.2020.9263118.
[5] N. K. Mishra and M. E. Celebi, "An overview of melanoma detection in dermoscopy images using image processing and machine learning," 2016.
[6] K. Thurnhofer-Hemsi and E. Domínguez, "A convolutional neural network framework for accurate skin cancer detection," Neural Process Lett., vol. 53, pp. 3073–3093, 2021, doi: 10.1007/s11063-020-10364-y.
[7] E. Farea, R. A. A. Saleh, H. AbuAlkebash, A. A. R. Farea, and M. A. Al-antari, "A hybrid deep learning skin cancer prediction framework," Eng. Sci. Technol., an Int. J., vol. 57, Sep. 2024, doi: 10.1016/j.jestch.2024.101818.
[8] A. Abobakir and A. Abdulazeez, "A review on utilizing machine learning classification algorithms for skin cancer," J. Appl. Sci. Technol. Trends, vol. 5, no. 2, pp. 60–71, Aug. 2024, doi: 10.38094/jastt52191.
[9] H. Bhatt, V. Shah, K. Shah, R. Shah, and M. Shah, "State-of-the-art machine learning techniques for melanoma skin cancer detection and classification: A comprehensive review," Chinese Med. Assoc., Aug. 2023, doi: 10.1016/j.imed.2022.08.004.
[10] N. Melarkode, K. Srinivasan, S. M. Qaisar, and P. Plawiak, "AI-powered diagnosis of skin cancer: A contemporary review, open challenges and future research directions," MDPI Cancers, vol. 15, no. 4, Feb. 2023, doi: 10.3390/cancers15041183.
[11] V. A. O. Nancy, P. Prabhavathy, M. S. Arya, and B. S. Ahamed, "Comparative study and analysis on skin cancer detection using machine learning and deep learning algorithms," Multimed. Tools Appl., vol. 82, no. 29, pp. 45913–45957, Dec. 2023, doi: 10.1007/s11042-023-16422-6.
[12] F. S. Prity et al., "RvXmBlendNet: A multi-architecture hybrid model for improved skin cancer detection," Human-Centric Intell. Syst., 2024, doi: 10.1007/s44230-024-00083-1.
[13] S. Jain, V. Jagtap, and N. Pise, "Computer-aided melanoma skin cancer detection using image processing," Procedia Comput. Sci., vol. 48, pp. 735–740, 2015, doi: 10.1016/j.procs.2015.04.209.
[14] H. Alquran et al., "The melanoma skin cancer detection and classification using support vector machine," in Proc. IEEE Jordan Conf. Appl. Elect. Eng. Comput. Technol. (AEECT), Aqaba, Jordan, 2017, pp. 1–5, doi: 10.1109/AEECT.2017.8257738.
[15] 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 Proc. 5th Int. Conf. Advanced Technol. Signal Image Process. (ATSIP), Sousse, Tunisia, 2020, pp. 1–5, doi: 10.1109/ATSIP49331.2020.9231544.
[16] M. Dildar et al., "Skin cancer detection: A review using deep learning techniques," Int. J. Environ. Res. Public Health, vol. 18, no. 10, p. 5479, 2021, doi: 10.3390/ijerph18105479.
[17] H. Nahata and S. P. Singh, "Deep learning solutions for skin cancer detection and diagnosis," in Machine Learning with Health Care Perspective, V. Jain and J. Chatterjee, Eds. Springer, Cham, 2020, doi: 10.1007/978-3-030-40850-3_8.
[18] M. Vidya and M. V. Karki, "Skin cancer detection using machine learning techniques," in Proc. IEEE Int. Conf. Electron. Comput. Commun. Technol. (CONECCT), Bangalore, India, 2020, pp. 1–5, doi: 10.1109/CONECCT50063.2020.9198489.
[19] A. H. Alasadi and B. M. ALsafy, "Early detection and classification of melanoma skin cancer," I.J. Inf. Technol. Comput. Sci., vol. 12, pp. 67–74, 2015, doi: 10.5815/ijitcs.2015.12.08.
[20] V. Narayanamurthy et al., "Skin cancer detection using non-invasive techniques," RSC Adv., vol. 8, no. 49, pp. 28095–28130, 2018.
[21] 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, p. 1183, 2022, doi: 10.3390/healthcare10071183.
Cite This Article
Choose your preferred format