Skin cancer detection : spreading awareness through mobile application

Skin cancer is a growing danger, especially the elderly population. Early detection and treatment is critical in reducing the fatality of melanoma. Screening done by a specialist or individual can lead to improved survival rate of melanoma as melanoma is detected at reduced thickness and is easie...

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Main Author: Christianti, Gabriella Benedicta
Other Authors: Owen Noel Newton Fernando
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2021
Subjects:
Online Access:https://hdl.handle.net/10356/148182
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1481822021-04-26T05:19:10Z Skin cancer detection : spreading awareness through mobile application Christianti, Gabriella Benedicta Owen Noel Newton Fernando School of Computer Science and Engineering OFernando@ntu.edu.sg Engineering::Computer science and engineering Skin cancer is a growing danger, especially the elderly population. Early detection and treatment is critical in reducing the fatality of melanoma. Screening done by a specialist or individual can lead to improved survival rate of melanoma as melanoma is detected at reduced thickness and is easier to treat. However, only a small fraction of individuals have performed a full body skin examination at least once in their lives as they lack education on their risks of developing skin cancer. This project aims to spread awareness on individual’s risk of developing skin cancer through four assessments: Skin Cancer Detection Algorithm, Skin Risk Assessment, Profile Risk Assessment, and Lesion Risk Assessments. With increased awareness, the general public is expected to be more cautious of suspicious skin lesions. This increase in caution is expected to encourage the general public to go for a check-up with a specialist earlier, thus facilitating early detection and treatment of skin cancer and reducing the mortality of skin cancer. An ensemble machine learning model is created by combining InceptionResNetV2 and DenseNet201 trained on the HAM10000 dataset. This ensemble model achieved an average macro-average accuracy of 86% and is used as the Skin Cancer Detection Algorithm. The main findings of the study are as follows: (1) The users find the application useful and easy to use, (2) The users display positive attitude and behavioural intention towards using the application, and (3) The usefulness and usability of the application both significantly positively influenced the Attitude and Behavioural Intention towards using the application. Bachelor of Engineering (Computer Science) 2021-04-26T05:19:10Z 2021-04-26T05:19:10Z 2021 Final Year Project (FYP) Christianti, G. B. (2021). Skin cancer detection : spreading awareness through mobile application. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148182 https://hdl.handle.net/10356/148182 en SCSE20-0122 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
spellingShingle Engineering::Computer science and engineering
Christianti, Gabriella Benedicta
Skin cancer detection : spreading awareness through mobile application
description Skin cancer is a growing danger, especially the elderly population. Early detection and treatment is critical in reducing the fatality of melanoma. Screening done by a specialist or individual can lead to improved survival rate of melanoma as melanoma is detected at reduced thickness and is easier to treat. However, only a small fraction of individuals have performed a full body skin examination at least once in their lives as they lack education on their risks of developing skin cancer. This project aims to spread awareness on individual’s risk of developing skin cancer through four assessments: Skin Cancer Detection Algorithm, Skin Risk Assessment, Profile Risk Assessment, and Lesion Risk Assessments. With increased awareness, the general public is expected to be more cautious of suspicious skin lesions. This increase in caution is expected to encourage the general public to go for a check-up with a specialist earlier, thus facilitating early detection and treatment of skin cancer and reducing the mortality of skin cancer. An ensemble machine learning model is created by combining InceptionResNetV2 and DenseNet201 trained on the HAM10000 dataset. This ensemble model achieved an average macro-average accuracy of 86% and is used as the Skin Cancer Detection Algorithm. The main findings of the study are as follows: (1) The users find the application useful and easy to use, (2) The users display positive attitude and behavioural intention towards using the application, and (3) The usefulness and usability of the application both significantly positively influenced the Attitude and Behavioural Intention towards using the application.
author2 Owen Noel Newton Fernando
author_facet Owen Noel Newton Fernando
Christianti, Gabriella Benedicta
format Final Year Project
author Christianti, Gabriella Benedicta
author_sort Christianti, Gabriella Benedicta
title Skin cancer detection : spreading awareness through mobile application
title_short Skin cancer detection : spreading awareness through mobile application
title_full Skin cancer detection : spreading awareness through mobile application
title_fullStr Skin cancer detection : spreading awareness through mobile application
title_full_unstemmed Skin cancer detection : spreading awareness through mobile application
title_sort skin cancer detection : spreading awareness through mobile application
publisher Nanyang Technological University
publishDate 2021
url https://hdl.handle.net/10356/148182
_version_ 1698713674249666560