MelanomaNet: An Effective Network for Melanoma Detection
| Autor: | Rian Huang, Feng Zhou, Nina Cheng, Baiying Lei, Tianfu Wang, Feng Jiang, Jiajun Liang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Skin Neoplasms
Artificial neural network Computer science business.industry Melanoma 0206 medical engineering Pattern recognition Dermoscopy 02 engineering and technology medicine.disease 020601 biomedical engineering Melanoma detection Support vector machine 0202 electrical engineering electronic engineering information engineering medicine Humans 020201 artificial intelligence & image processing Artificial intelligence Skin lesion business Classifier (UML) Algorithms |
| Zdroj: | EMBC |
| ISSN: | 2694-0604 |
| Popis: | Melanoma is one of the most deadly skin lesion, which often uses the skin dermoscopy to detect it. However, the low interclass variations between melanoma images make manual dermoscopic detection time-consuming and laborious. Therefore, an automatic recognition algorithm of skin image is highly desirable. However, the traditional methods still have the limitations (e.g., weak robustness and generalization ability). To meet the challenge, we propose an effective architecture based on residual – squeeze – and - excitation -Inception-v4 network (MelanomaNet) to detect melanoma. Specifically, Inception-v4 structure is utilized to get the rich spatial features and increase feature diversity. We also consider the relationship between feature channels by adding residual-squeeze-and-excitation (RSE) blocks in Inception- v4 network using the feature recalibration strategies. Finally, we use the support vector machine (SVM) as the classifier for the skin lesion classification. We evaluate our proposed method on the public available ISIC skin lesion challenge datasets in 2018 for training and evaluation. The experimental results show that the proposed method has achieved better performance over the state-of-the-arts methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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