华中科技大学学报（自然科学版）2024，Vol.52Issue(5)：90-97,8.DOI:10.13245/j.hust.240576
改进型DeepLabV3+的糖尿病眼底病变分割
Diabetic fundus lesion segmentation by improved DeepLabV3+
摘要
Abstract
Aiming at the problem of difficulties of multi-class segmentation and low recognition rate for small lesions in diabetic retinopathy,a method combining attention mechanism with an improved DeepLabV3+model was proposed for multi-class lesion segmentation.First,the MobileNetV2 network was employed to extract lesion features,reducing parameter count and enhancing training speed.Subsequently,the dilation convolution layers and dilation rates in the atrous spatial pyramid pooling were optimized to improve the capability of capturing features of small lesions.Then,improvements were made to the DeepLabV3+model by incorporating a coordinate attention mechanism to perceive lesion direction and position information,thereby enhancing recognition accuracy.Finally,the proposed model was trained and tested on the fine-grained annotated diabetic retinopathy(FGADR)and Indian diabetic retinopathy image dataset(IDRiD).Experimental results show that the proposed method achieves a mean intersection over union(MIoU)metric of 73.75%,showcasing high segmentation accuracy,and confirming the effectiveness of the model.关键词
糖尿病视网膜眼底病变/深度学习/DeepLabV3+网络/坐标注意力/多类分割Key words
diabetic retinopathy/deep learning/DeepLabV3+network/coordinate attention/multiple class segmentation分类
信息技术与安全科学引用本文复制引用
马晓普,刘文涛,李贺..改进型DeepLabV3+的糖尿病眼底病变分割[J].华中科技大学学报（自然科学版）,2024,52(5):90-97,8.基金项目
国家自然科学基金资助项目(62002180) （62002180）
南阳师范学院"卧龙学者"奖励计划支持项目基金资助项目. （）
