Big transfer learning for automated skin cancer classification
Skin cancer is an example of the most dangerous disease. Early diagnosis of skin cancer can save many people's lives. Manual classification methods are time-consuming and costly. Deep learning has been proposed for the automated classification of skin cancer. Although deep learning showed impre...
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| Main Authors: | , , |
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2021-09-01.
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| Online Access: | Get fulltext |
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| LEADER | 02548 am a22003133u 4500 | ||
|---|---|---|---|
| 001 | ijeecs25756_15436 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Arkah, Zinah Mohsin |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Al-Dulaimi, Dalya S. |e author |
| 700 | 1 | 0 | |a Khekan, Ahlam R. |e author |
| 245 | 0 | 0 | |a Big transfer learning for automated skin cancer classification |
| 260 | |b Institute of Advanced Engineering and Science, |c 2021-09-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/25756 | ||
| 520 | |a Skin cancer is an example of the most dangerous disease. Early diagnosis of skin cancer can save many people's lives. Manual classification methods are time-consuming and costly. Deep learning has been proposed for the automated classification of skin cancer. Although deep learning showed impressive performance in several medical imaging tasks, it requires a big number of images to achieve a good performance. The skin cancer classification task suffers from providing deep learning with sufficient data due to the expensive annotation process and required experts. One of the most used solutions is transfer learning of pre-trained models of the ImageNet dataset. However, the learned features of pre-trained models are different from skin cancer image features. To end this, we introduce a novel approach of transfer learning by training the pre-trained models of the ImageNet (VGG, GoogleNet, and ResNet50) on a large number of unlabelled skin cancer images, first. We then train them on a small number of labeled skin images. Our experimental results proved that the proposed method is efficient by achieving an accuracy of 84% with ResNet50 when directly trained with a small number of labeled skin and 93.7% when trained with the proposed approach. | ||
| 540 | |a Copyright (c) 2021 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a CNN; Deep learning; Pre-trained models; Skin cancer; Transfer learning; | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 23, No 3: September 2021; 1611-1619 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v23.i3 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/25756/15436 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/25756/15436 |z Get fulltext |