Enhancing Early Detection of Melanoma: A Deep Learning Approach for Skin Cancer Prediction

Md Sadi al Huda - American International University-Bangladesh,AIUB, 408/1 Kuratoli
Md. Asraf Ali - American International University-Bangladesh,AIUB, 408/1 Kuratoli
Ajran Hossain - American International University-Bangladesh,AIUB, 408/1 Kuratoli
Fatama Tuz Johora - Artificial Intelligence Research And Innovation Lab,15/C, Block E, Asad Avenue, Mohammadpur, Dhaka-1207
Tze Hui Liew - Multimedia University, Malaysia
Ridwan Jamal Sadib - American International University Bangladesh,AIUB, 408/1 Kuratoli
Md. Jakir Hossen - Multimedia University, Melaka, Malaysia
Nasim Ahmed - The University of Sydney, Sydney, Australia


Citation Format:



DOI: http://dx.doi.org/10.62527/joiv.8.3-2.3081

Abstract


Melanoma, a form of skin cancer, is a substantial global public health threat due to its rising prevalence and the potential for severe outcomes if not promptly identified and managed. Detecting skin cancer lesions in their first stages enhances patient outcomes and decreases mortality rates. The core issue investigated in this research paper is the enduring problem of early skin cancer prediction. In the past, individuals often lacked awareness of their skin cancer condition until it had reached late stages. Consequently, this resulted in delayed diagnoses, which restricted the available treatment options and perhaps led to worse outcomes.  This research focuses on finding key attributes and methods in a specialized dataset to effectively differentiate between benign and potentially malignant skin lesions, particularly the implementation of an early-stage skin cancer prediction model. It aims to accurately categorize skin mole pictures as benign or malignant using a Convolutional Neural Network (CNN) model built within the PyTorch framework. The primary aim of this study was to enhance the accuracy and effectiveness of diagnosing skin problems by implementing deep learning algorithms to automate the process of showing such conditions. The model underwent training using 3600 skin mole images sourced from the ISIC-Archive on a GPU RTX 3080. Its outstanding performance is shown by an F1 score of 0.8496 and an accuracy rate of 85%. This research aims to create a predictive model and offer a practical solution that healthcare professionals can readily use for early skin cancer prediction.

Keywords


Deep learning; CNN; PyTorch; early detection; malignancy prediction; skin cancer; automated diagnosis

Full Text:

PDF

References


World Health Organisation, “Radiation: Ultraviolet (UV) radiation and skin cancer,” Radiation: Ultraviolet (UV) radiation and skin cancer 16 October 2017 | Q&A. Accessed: Oct. 20, 2024. [Online]. Available: https://www.who.int/news-room/questions-and-answers/item/radiation-ultraviolet-(uv)-radiation-and-skin-cancer

“Skin Cancer Information - The Skin Cancer Foundation.” Accessed: Oct. 20, 2024. [Online]. Available: https://www.skincancer.org/skin-cancer-information/

Robinmarksm B B S, “An Overview of Skin Cancers Incidence and Causation”, doi: 10.1002/1097-0142(19950115)75:2.

“Skin Cancer (Including Melanoma)—Patient Version - NCI.” Accessed: Oct. 20, 2024. [Online]. Available: https://www.cancer.gov/types/skin

“Information and Resources about Cancer: Breast, Colon, Lung, Prostate, Skin | American Cancer Society.” Accessed: Oct. 20, 2024. [Online]. Available: https://www.cancer.org/

S. Khattar and R. Kaur, “Computer assisted diagnosis of skin cancer: A survey and future recommendations,” Computers and Electrical Engineering, vol. 104, Dec. 2022, doi: 10.1016/J.COMPELECENG.2022.108431.

“Skin Cancer - Cancer Center.ai - AI i Platform in Oncology and Pathology.” Accessed: Oct. 20, 2024. [Online]. Available: https://cancercenter.ai/skin-cancer/

C. Fortes et al., “A protective effect of the Mediterranean diet for cutaneous melanoma,” International Journal of Epidemiology, vol. 37, no. 5, pp. 1018–1029, 2008, doi: 10.1093/IJE/DYN132.

S. Wróbel, M. Przybyło, and E. Stȩpień, “The Clinical Trial Landscape for Melanoma Therapies,” Journal of clinical medicine, vol. 8, no. 3, Mar. 2019, doi: 10.3390/JCM8030368.

D. Schadendorf et al., “Melanoma,” Lancet (London, England), vol. 392, no. 10151, pp. 971–984, Sep. 2018, doi: 10.1016/S0140-6736(18)31559-9.

“Melanoma Skin Cancer Statistics | American Cancer Society.” Accessed: Oct. 20, 2024. [Online]. Available: https://www.cancer.org/cancer/types/melanoma-skin-cancer/about/key-statistics.html

M. A. O’Leary and S. J. Wang, “Epidemiology and Prevention of Cutaneous Cancer,” Otolaryngologic Clinics of North America, vol. 54, no. 2, pp. 247–257, Apr. 2021, doi: 10.1016/J.OTC.2020.11.001.

T. Petrie, R. Samatham, A. M. Witkowski, A. Esteva, and S. A. Leachman, “Melanoma Early Detection: Big Data, Bigger Picture,” Journal of Investigative Dermatology, vol. 139, no. 1, pp. 25–30, Jan. 2019, doi: 10.1016/J.JID.2018.06.187.

T. C. Pham, C. M. Luong, V. D. Hoang, and A. Doucet, “AI outperformed every dermatologist in dermoscopic melanoma diagnosis, using an optimized deep-CNN architecture with custom mini-batch logic and loss function,” Scientific Reports 2021 11:1, vol. 11, no. 1, pp. 1–13, Sep. 2021, doi: 10.1038/s41598-021-96707-8.

M. Subramanian, M. A. A. Walid, S. P. Mallick, R. Rastogi, A. Chauhan, and A. Vidya, “Melanoma Skin Cancer Detection using a CNN-Regularized Extreme Learning Machine (RELM) based Model,” Proceedings of the 2023 2nd International Conference on Electronics and Renewable Systems, ICEARS 2023, pp. 1239–1245, 2023, doi: 10.1109/ICEARS56392.2023.10085489.

G. I. Sayed, M. M. Soliman, and A. E. Hassanien, “A novel melanoma prediction model for imbalanced data using optimized SqueezeNet by bald eagle search optimization,” Computers in Biology and Medicine, vol. 136, p. 104712, Sep. 2021, doi: 10.1016/J.COMPBIOMED.2021.104712.

A. K. Waweru, K. Ahmed, Y. Miao, and P. Kawan, “Deep Learning in Skin Lesion Analysis towards Cancer Detection,” Proceedings of the International Conference on Information Visualisation, vol. 2020-September, pp. 740–745, Sep. 2020, doi: 10.1109/IV51561.2020.00130.

R. Ashraf, I. Kiran, T. Mahmood, A. Ur Rehman Butt, N. Razzaq, and Z. Farooq, “An efficient technique for skin cancer classification using deep learning,” Proceedings - 2020 23rd IEEE International Multi-Topic Conference, INMIC 2020, Nov. 2020, doi: 10.1109/INMIC50486.2020.9318164.

F. W. Alsaade, T. H. H. Aldhyani, and M. H. Al-Adhaileh, “Developing a Recognition System for Diagnosing Melanoma Skin Lesions Using Artificial Intelligence Algorithms,” Computational and Mathematical Methods in Medicine, vol. 2021, no. 1, p. 9998379, Jan. 2021, doi: 10.1155/2021/9998379.

S. Labde and N. Vanjari, “Prediction of Skin Cancer Using CNN,” 2022 3rd International Conference for Emerging Technology, INCET 2022, 2022, doi: 10.1109/INCET54531.2022.9825093.

M. Rosas-Lara, J. C. Mendoza-Tello, A. Flores, and G. Zumba-Acosta, “A Convolutional Neural Network-Based Web Prototype to Support Melanoma Skin Cancer Detection,” Proceedings - 3rd International Conference on Information Systems and Software Technologies, ICI2ST 2022, pp. 1–7, 2022, doi: 10.1109/ICI2ST57350.2022.00008.

M. Babar, R. T. Butt, H. Batool, M. A. Asghar, A. R. Majeed, and M. J. Khan, “A Refined Approach for Classification and Detection of Melanoma Skin Cancer using Deep Neural Network,” 2021 International Conference on Digital Futures and Transformative Technologies, ICoDT2 2021, May 2021, doi: 10.1109/ICODT252288.2021.9441520.

R. S. Sanketh, M. Madhu Bala, P. V. N. Reddy, and G. V. S. Phani Kumar, “Melanoma Disease Detection Using Convolutional Neural Networks,” Proceedings of the International Conference on Intelligent Computing and Control Systems, ICICCS 2020, pp. 1031–1037, May 2020, doi: 10.1109/ICICCS48265.2020.9121075.

“ISIC | International Skin Imaging Collaboration.” Accessed: Oct. 20, 2024. [Online]. Available: https://www.isic-archive.com/

N. Fahad, K. O. M. Goh, Md. I. Hossen, C. Tee, and Md. A. Ali, “Building a Fortress Against Fake News: Harnessing the Power of Subfields in Artificial Intelligence,” Journal of Telecommunications and the Digital Economy, vol. 11, no. 3, pp. 68–83, Sep. 2023, doi: 10.18080/jtde.v11n3.765.

K. Tanvir et al., “Enhancing Early-Stage Detection of Melanoma using a Hybrid BiTDense,” TWIST, vol. 19, no. 2, pp. 298–305, May 2024, doi: 10.5281/ZENODO.10049652.

E. Mahamud, N. Fahad, M. Assaduzzaman, S. M. Zain, K. O. M. Goh, and M. K. Morol, “An explainable artificial intelligence model for multiple lung diseases classification from chest X-ray images using fine-tuned transfer learning,” Decision Analytics Journal, vol. 12, Sep. 2024, doi: 10.1016/j.dajour.2024.100499.

N. Hossain, N. Fahad, R. Ahmed, A. Sen, S. AL Huda, and I. Hossen, “Preventing Student’s Mental Health Problems with the Help of Data Mining,” Article in International Journal of Computing, vol. 23, no. 1, p. 2024, 2024, doi: 10.47839/ijc.23.1.3441.

M. Asraf Ali et al., “Exploring a Novel Machine Learning Approach for Evaluating Parkinson’s Disease, Duration, and Vitamin D Level,” Article in International Journal of Advanced Computer Science and Applications, vol. 14, no. 12, p. 2023, 2023, doi: 10.14569/IJACSA.2023.0141265.

R. Ahmed et al., “A novel integrated logistic regression model enhanced with recursive feature elimination and explainable artificial intelligence for dementia prediction,” Healthcare Analytics, vol. 6, Dec. 2024, doi: 10.1016/j.health.2024.100362.

N. Fahad, A. Sen, S. S. Jisha, S. Ahmad, H. Mokhlis, and M. S. Hossain, “Identification of Human Movement through a Novel Machine Learning Approach,” in 2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023, Institute of Electrical and Electronics Engineers Inc., 2023. doi: 10.1109/I-PACT58649.2023.10434296.

N. Fahad et al., “Stand up Against Bad Intended News: An Approach to Detect Fake News using Machine Learning,” Emerging Science Journal, vol. 7, no. 4, pp. 1247–1259, Aug. 2023, doi: 10.28991/ESJ-2023-07-04-015.

C.C. Chai, W.H. Khoh, Y.H. Pang, and H.Y. Yap, “A Lung Cancer Detection with Pre-Trained CNN Models,” Journal of Informatics and Web Engineering, vol. 3, no. 1, pp. 41–54, Feb. 2024, doi: https://doi.org/10.33093/jiwe.2024.3.1.3.

Y. H. Gan, S. Y. Ooi, Y. H. Pang, Y. H. Tay, and Q. F. Yeo, “Facial Skin Analysis in Malaysians using YOLOv5: A Deep Learning Perspective,” Journal of Informatics and Web Engineering, vol. 3, no. 2, pp. 1–18, Jun. 2024, doi: https://doi.org/10.33093/jiwe.2023.3.2.1.