ProMorph: Melanoma Classification in Dermoscopic Images using Proportional Morphological Dilation and Convolutional Neural Networks

Author:

Abante Arlan Floyd1ORCID,Caasi Michael Angelo1ORCID,Espinosa Rio1ORCID,Vinzon Jerielle1ORCID,Suarez Jessie James1ORCID

Affiliation:

1. Department of Computer Science, University of Santo Tomas, Philippines

Publisher

ACM

Reference19 articles.

1. IARC. (2022). Melanoma Awareness Month 2022. World Health Organization. https://www.iarc.who.int/news-events/melanoma-awareness-month-2022/

2. Codella, N., Rotemberg, V., Tschandl, P., Celebi, M. E., Dusza, S., Gutman, D., ... & Halpern, A. (2019). Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic). arXiv preprint arXiv:1902.03368.

3. Wang, G., Yan, P., Tang, Q., Yang, L., & Chen, J. (2023). Multiscale Feature Fusion for Skin Lesion Classification. BioMed Research International,2023. https://www.hindawi.com/journals/bmri/2023/5146543/

4. Fusion-based skin detection using image distribution model

5. Ecker R. Ellinger I. Langs G. Mahbod A. & Tschandl P. (2020 August 26). The effects of skin lesion segmentation on the performance of Dermatoscopic Image Classification. Computer Methods and Programs in Biomedicine. https://www.sciencedirect.com/science/article/pii/S0169260720315583

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