doi: 10.18178/ijml.2025.15.2.1176
Melanoma Detection Using Convolutional Neural Network
Pooja Illangarathne*
,
Nethari Jayasinghe ,
Sharith Rodrigo ,
Kanishka Hewageegana ,
Prasad Wimalaratne
- School of Computing, Informatics Institute of Technology, Colombo, Sri Lanka
- Manuscript receivedNovember 27, 2024
- acceptedFebruary 12, 2025
- publishedApril 24, 2025
Abstract
Melanoma, a severe kind of skin cancer, requires early identification to enhance patient outcomes. This paper describes a unique approach to melanoma diagnosis that combines image processing techniques with deep learning methodologies. This paper provides a method for analyzing skin lesion photos that uses a mix of color, texture, and form factors, followed by classification using a convolutional neural network. Using a publicly accessible collection of skin lesion photos, this method obtained a 93% accuracy in differentiating between malignant and benign lesions. These positive findings suggest that this study approach has the potential to considerably benefit dermatologists in the early detection of melanoma, improving treatment outcomes and patient survival.
Keywords
- dermatology
- melanoma
- skin cancer
- machine learning
- Gaussian mixture model
- backpropagation neural network
- integrated development environment
How to Cite
Pooja Illangarathne, Nethari Jayasinghe, Sharith Rodrigo, Kanishka Hewageegana, and Prasad Wimalaratne, "Melanoma Detection Using Convolutional Neural Network," International Journal of Machine Learning, vol. 15, no. 2, pp. 34-39, 2025. https://doi.org/10.18178/ijml.2025.15.2.1176
Copyright & License
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).