Huang, Junjun and Saw, Shier Nee and He, Tianran and Yang, Ruohan and Qin, Yesheng and Chen, Yanlin and Kiong, Loo Chu (2024) DCNNLFS: A Dilated Convolutional Neural Network With Late Fusion Strategy for Intelligent Classification of Gastric Histopathology Images. IEEE Journal of Biomedical and Health Informatics, 28 (8). pp. 4534-4543. ISSN 2168-2194, DOI https://doi.org/10.1109/JBHI.2023.3334709.
Full text not available from this repository.Abstract
Gastric cancer has a high incidence rate, significantly threatening patients' health. Gastric histopathology images can reliably diagnose related diseases. Still, the data volume of histopathology images is too large, making misdiagnosis or missed diagnosis easy. The classification model based on deep learning has made some progress on gastric histopathology images. However, traditional convolutional neural networks (CNNs) generally use pooling operations, which will reduce the spatial resolution of the image, resulting in poor prediction results. The image feature in previous CNN has a poor perception of details. Therefore, we design a dilated CNN with a late fusion strategy (DCNNLFS) for gastric histopathology image classification. The DCNNLFS model utilizes dilated convolutions, enabling it to expand the receptive field. The dilated convolutions can learn the different contextual information by adjusting the dilation rate. The DCNNLFS model uses a late fusion strategy to enhance the classification ability of DCNNLFS. We run related experiments on a gastric histopathology image dataset to verify the excellence of the DCNNLFS model, where the three metrics Precision, Accuracy, and F1-Score are 0.938, 0.935, and 0.959.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Histopathology; Feature extraction; Convolutional neural networks; Cancer; Spatial resolution; Deep learning; Computational modeling; Gastric histopathology image; dilated convolution; late fusion strategy; classification model |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 09 Apr 2025 03:07 |
| Last Modified: | 09 Apr 2025 03:07 |
| URI: | http://eprints.um.edu.my/id/eprint/46728 |
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