Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/149427
Title: Skin lesion (melanoma) segmentation
Authors: Ong, Shi Quan
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Electrical and electronic engineering
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Ong, S. Q. (2021). Skin lesion (melanoma) segmentation. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/149427
Project: B3104-21
Abstract: Skin lesions can pose serious health problems if left undetected and untreated. There are detection methods such as excisional biopsy as well as dermoscopy. However, there has been a demand of automating and aiding doctors with their diagnosis through Artificial Intelligence. The aim of this project is to evaluate the effectiveness of a Deep Learning model, U-NET, in identifying and segmenting the skin lesions from the ISIC 2017 Challenge Dataset. The evaluation consists of testing the U-NET model’s predicted segmentation accuracy with respect to different image and lesion types. Through this project, it can be concluded that the U-NET is effective in producing accurate segmentations of skin lesions.
URI: https://hdl.handle.net/10356/149427
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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