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Skin Lesion Detection Using Deep Neural Network By Smart Handheld Devices

Early detection of malignant skin lesions improves patient survival rates. Conventional self-detection method for public possess subjectivity, inaccuracy, and require experience. The goal of this project is to develop an Android based mobile application with object detection deep learning integratio...

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Bibliographic Details
Main Author: Tan, Hou Ren
Format: Final Year Project / Dissertation / Thesis
Published: 2020
Subjects:
R Medicine (General)
Online Access:http://eprints.utar.edu.my/4225/1/1700832_FYP_report_%2D_HOU_REN_TAN.pdf
http://eprints.utar.edu.my/4225/
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spelling my-utar-eprints.42252021-08-18T12:15:33Z Skin Lesion Detection Using Deep Neural Network By Smart Handheld Devices Tan, Hou Ren R Medicine (General) Early detection of malignant skin lesions improves patient survival rates. Conventional self-detection method for public possess subjectivity, inaccuracy, and require experience. The goal of this project is to develop an Android based mobile application with object detection deep learning integration that allows global users to perform malignant skin lesions self-detection easily using a smartphone, for overcoming the limitations of the conventional method. Transfer Learning has been performed on various object detection models using ISIC skin lesions dataset with TensorFlow Object Detection API. The selected object detection model is SSD MobileNet V2 with 93.9% of evaluation accuracy after training due to its lightweight architecture therefore suitable for smartphone integration. The selected model has surpassed existing classification model in terms of accuracy after validation with a new dataset. A mobile application has been developed successfully with Android Studio. The trained object detection model successfully integrated into the mobile application using Firebase ML Kit and has achieved low detection time on smartphones. The mobile application has been proven to be compatible with various Android versions and screen sizes after tested with 7 different smartphones using Firebase Test Lab. 2020 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/4225/1/1700832_FYP_report_%2D_HOU_REN_TAN.pdf Tan, Hou Ren (2020) Skin Lesion Detection Using Deep Neural Network By Smart Handheld Devices. Final Year Project, UTAR. http://eprints.utar.edu.my/4225/
institution Universiti Tunku Abdul Rahman
building UTAR Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tunku Abdul Rahman
content_source UTAR Institutional Repository
url_provider http://eprints.utar.edu.my
topic R Medicine (General)
spellingShingle R Medicine (General)
Tan, Hou Ren
Skin Lesion Detection Using Deep Neural Network By Smart Handheld Devices
description Early detection of malignant skin lesions improves patient survival rates. Conventional self-detection method for public possess subjectivity, inaccuracy, and require experience. The goal of this project is to develop an Android based mobile application with object detection deep learning integration that allows global users to perform malignant skin lesions self-detection easily using a smartphone, for overcoming the limitations of the conventional method. Transfer Learning has been performed on various object detection models using ISIC skin lesions dataset with TensorFlow Object Detection API. The selected object detection model is SSD MobileNet V2 with 93.9% of evaluation accuracy after training due to its lightweight architecture therefore suitable for smartphone integration. The selected model has surpassed existing classification model in terms of accuracy after validation with a new dataset. A mobile application has been developed successfully with Android Studio. The trained object detection model successfully integrated into the mobile application using Firebase ML Kit and has achieved low detection time on smartphones. The mobile application has been proven to be compatible with various Android versions and screen sizes after tested with 7 different smartphones using Firebase Test Lab.
format Final Year Project / Dissertation / Thesis
author Tan, Hou Ren
author_facet Tan, Hou Ren
author_sort Tan, Hou Ren
title Skin Lesion Detection Using Deep Neural Network By Smart Handheld Devices
title_short Skin Lesion Detection Using Deep Neural Network By Smart Handheld Devices
title_full Skin Lesion Detection Using Deep Neural Network By Smart Handheld Devices
title_fullStr Skin Lesion Detection Using Deep Neural Network By Smart Handheld Devices
title_full_unstemmed Skin Lesion Detection Using Deep Neural Network By Smart Handheld Devices
title_sort skin lesion detection using deep neural network by smart handheld devices
publishDate 2020
url http://eprints.utar.edu.my/4225/1/1700832_FYP_report_%2D_HOU_REN_TAN.pdf
http://eprints.utar.edu.my/4225/
_version_ 1709672667522531328
score 13.222552

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