Skin lesion detection and recognition via deep learning
Melanoma, also known as malignant melanoma, is a type of cancer that develops from melanocytes, it is a rare form of skin cancer and arguably the most dangerous form of skin cancer. Melanoma is one of the leading causes of death due to its high degree of malignancy. Besides, some melanomas have a...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/157854 |
| _version_ | 1811692738375057408 |
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| author | Chen, Ziyu |
| author2 | Jiang Xudong |
| author_facet | Jiang Xudong Chen, Ziyu |
| author_sort | Chen, Ziyu |
| collection | NTU |
| description | Melanoma, also known as malignant melanoma, is a type of cancer that develops from
melanocytes, it is a rare form of skin cancer and arguably the most dangerous form of
skin cancer. Melanoma is one of the leading causes of death due to its high degree of
malignancy. Besides, some melanomas have a low contrast to the adjacent skin and
are difficult for dermatologists to do physical detection and examination, so it is rather
challenging to automatically apply segmentation techniques to them. This report
proposes a medical image segmentation model and classification implementation
model that will speed up the segmentation and diagnosis of melanoma.
The image segmentation implementation of this project is constructed based on ISIC
2018 Task1 dataset that contains 2625 images. A network called U-Net variant based
on a fully convolutional network (FCN) is used, which obtained 93.3% of Accuracy,
87.1% of Precision, 90.5% of Recall, 87.3% of F1, 85.1% Dice coefficient, and 79.3%
of Jaccard.
The image classification implementation of this project is constructed based on ISIC
2018 Task3 dataset that contains 10046 images. A Convolutional Neural Network
(CNN) is used, which obtained an evaluation accuracy of 96.47%, specificity of 98.3%,
the sensitivity of 90.3%, and precision of 92.2%. |
| first_indexed | 2024-10-01T06:40:33Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/157854 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:40:33Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1578542023-07-07T19:04:37Z Skin lesion detection and recognition via deep learning Chen, Ziyu Jiang Xudong School of Electrical and Electronic Engineering EXDJiang@ntu.edu.sg Engineering::Electrical and electronic engineering Melanoma, also known as malignant melanoma, is a type of cancer that develops from melanocytes, it is a rare form of skin cancer and arguably the most dangerous form of skin cancer. Melanoma is one of the leading causes of death due to its high degree of malignancy. Besides, some melanomas have a low contrast to the adjacent skin and are difficult for dermatologists to do physical detection and examination, so it is rather challenging to automatically apply segmentation techniques to them. This report proposes a medical image segmentation model and classification implementation model that will speed up the segmentation and diagnosis of melanoma. The image segmentation implementation of this project is constructed based on ISIC 2018 Task1 dataset that contains 2625 images. A network called U-Net variant based on a fully convolutional network (FCN) is used, which obtained 93.3% of Accuracy, 87.1% of Precision, 90.5% of Recall, 87.3% of F1, 85.1% Dice coefficient, and 79.3% of Jaccard. The image classification implementation of this project is constructed based on ISIC 2018 Task3 dataset that contains 10046 images. A Convolutional Neural Network (CNN) is used, which obtained an evaluation accuracy of 96.47%, specificity of 98.3%, the sensitivity of 90.3%, and precision of 92.2%. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-24T03:17:52Z 2022-05-24T03:17:52Z 2022 Final Year Project (FYP) Chen, Z. (2022). Skin lesion detection and recognition via deep learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157854 https://hdl.handle.net/10356/157854 en P3040-202 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Chen, Ziyu Skin lesion detection and recognition via deep learning |
| title | Skin lesion detection and recognition via deep learning |
| title_full | Skin lesion detection and recognition via deep learning |
| title_fullStr | Skin lesion detection and recognition via deep learning |
| title_full_unstemmed | Skin lesion detection and recognition via deep learning |
| title_short | Skin lesion detection and recognition via deep learning |
| title_sort | skin lesion detection and recognition via deep learning |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/157854 |
| work_keys_str_mv | AT chenziyu skinlesiondetectionandrecognitionviadeeplearning |