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Convolutional Neural Network with Multiscale Fusion and Attention Mechanism for Skin Diseases Assisted Diagnosis.
- Source :
-
Computational Intelligence & Neuroscience . 6/14/2022, p1-10. 10p. - Publication Year :
- 2022
-
Abstract
- Melanoma segmentation based on a convolutional neural network (CNN) has recently attracted extensive attention. However, the features captured by CNN are always local that result in discontinuous feature extraction. To solve this problem, we propose a novel multiscale feature fusion network (MSFA-Net). MSFA-Net can extract feature information at different scales through a multiscale feature fusion structure (MSF) in the network and then calibrate and restore the extracted information to achieve the purpose of melanoma segmentation. Specifically, based on the popular encoder-decoder structure, we designed three functional modules, namely MSF, asymmetric skip connection structure (ASCS), and calibration decoder (Decoder). In addition, a weighted cross-entropy loss and two-stage learning rate optimization strategy are designed to train the network more effectively. Compared qualitatively and quantitatively with the representative neural network methods with encoder-decoder structure, such as U-Net, the proposed method can achieve advanced performance. [ABSTRACT FROM AUTHOR]
- Subjects :
- *CONVOLUTIONAL neural networks
*SKIN disease diagnosis
*FEATURE extraction
Subjects
Details
- Language :
- English
- ISSN :
- 16875265
- Database :
- Academic Search Index
- Journal :
- Computational Intelligence & Neuroscience
- Publication Type :
- Academic Journal
- Accession number :
- 157445337
- Full Text :
- https://doi.org/10.1155/2022/8390997