抄録
Medical imaging plays an indispensable role in precise patient diagnosis. The integration of deep learning into medical diagnostics is becoming increasingly common. However, existing deep learning models face performance and efficiency challenges, especially in resource-constrained scenarios. To overcome these challenges, we introduce a novel dendritic neural efficientnet model called DEN, inspired by the function of brain neurons, which efficiently extracts image features and enhances image classification performance. Assessments on a diabetic retinopathy fundus image dataset reveal DEN’s superior performance compared to EfficientNet and other classical neural network models.
本文言語 | 英語 |
---|---|
ページ(範囲) | 1281-1284 |
ページ数 | 4 |
ジャーナル | IEICE Transactions on Information and Systems |
巻 | E107.D |
号 | 9 |
DOI | |
出版ステータス | 出版済み - 2024/09 |
ASJC Scopus 主題領域
- ソフトウェア
- ハードウェアとアーキテクチャ
- コンピュータ ビジョンおよびパターン認識
- 電子工学および電気工学
- 人工知能