ROI-based features for classification of skin diseases using a multi-layer neural network
Skin diseases have a serious impact on human life and health. This article aims to represent the classification accuracy of skin diseases for supporting the physicians' correct decision on patients for early treatment. In particular, 100 images in each type of five skin diseases from ISIC datab...
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2021-07-01.
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| Online Access: | Get fulltext |
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| LEADER | 02762 am a22003013u 4500 | ||
|---|---|---|---|
| 001 | ijeecs24428_15171 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Nguyen, Thanh-Hai |e author |
| 100 | 1 | 0 | |a HCMC University of Technology and Education, Vietnam |e contributor |
| 700 | 1 | 0 | |a Ngo, Ba-Viet |e author |
| 245 | 0 | 0 | |a ROI-based features for classification of skin diseases using a multi-layer neural network |
| 260 | |b Institute of Advanced Engineering and Science, |c 2021-07-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/24428 | ||
| 520 | |a Skin diseases have a serious impact on human life and health. This article aims to represent the classification accuracy of skin diseases for supporting the physicians' correct decision on patients for early treatment. In particular, 100 images in each type of five skin diseases from ISIC database are used for balanced datasets related to the classification accuracy. In addition, this paper focuses on processing images for extracting six optimal types of eleven features of skin disease image for higher classification performance and also this takes less time for training. Therefore, skin disease images are filtered and segmented for separating region of interests (ROIs) before extracting optimal features. First, the skin disease images are processed by normalizing sizes, removing noises, segmenting to separate region of interests (ROIs) showing skin disease signs. Next, a gray-level co-occurrence matrix (GLCM) method is applied for texture analysis to extract eleven features. With the optimal six features chosen, the high classification accuracy of skin diseases is about 92% evaluated using a matrix confusion. The result showed to illustrate the effectiveness of the proposed method. Furthermore, this method can be developed for other medical datasets for supporting in disease diagnosis. | ||
| 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 | |a Medical image processing for separating the best ROI; Skin disease classification with high accuracy; Balanced datasets of five types | ||
| 690 | |a GLCM algorithm; Matrix confusion; MLNN structure; ROI features; Skin diseases | ||
| 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 1: July 2021; 216-228 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v23.i1 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/24428/15171 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/24428/15171 |z Get fulltext |