摘要
为了准确、方便地识别多类型眼底病变,提出光学相干断层扫描技术(OCT)图像的轻量化分类模型MB-CNN.降低卷积核的使用个数,调节每个阶段卷积块的使用比例,设计轻量化主干网络L-Resnet,通过加深网络深度增强对深层语义信息的提取.使用深度可分离卷积设计多尺度卷积块MultiBlock,利用MultiBloc深度挖掘病灶区域的特征,使用不同的卷积核提取不同尺寸病变的特征,提高网络对病变OCT图像的识别能力.构建特征融合模块FFM,融合浅层信息和深层信息,充分提取病变特征的纹理和语义信息,提高对小目标病变的识别能力.实验结果显示,MB-CNN在UCSD、 Duke和NEH3个数据集上的总体分类精度分别达到97.2%、 99.92%和94.37%,模型参数量明显降低,所提模型能够针对眼底的多种病变进行分类.
A lightweight classification model MB-CNN for optical coherence tomography(OCT)images was proposed to accurately and conveniently identify multiple types of fundus lesions.By reducing the number of convolution cores and adjusting the proportion of convolution blocks in each stage,a lightweight backbone network L-Resnet was designed,and the extraction of deep-layer semantic information was enhanced by deepening the network depth.The multi-scale convolution block MultiBlock was designed using depthwise seperable convolution,and the features of the lesion area was mined.Different convolution kernels were used to extract the lesions features of different sizes to improve the recognition ability of the network to the OCT image of the lesion.The feature fusion module FFM was constructed,and the shallow layer information and deep layer information were fused,the texture and semantic information of the pathological features were extracted,and the recognition ability of small target lesions was improved.Experimental result showed that the overall classification accuracy of MB-CNN in the three datasets of UCSD,Duke and NEH was 97.2%,99.92%and 94.37%respectively,the amount of model parameters were significantly reduced.The proposed model can classify various fundus lesions.
作者
侯小虎
贾晓芬
赵佰亭
HOU Xiao-hu;JIA Xiao-fen;ZHAO Bai-ting(The First Affiliated Hospital of Anhui University of Science and Technology(Huainan First People's Hospital),Huainan 232001,China;Institute of Electrical and Information Engineering,Anhui University of Science and Technology,Huainan 232001,China;Institute of Artificial Intelligence,Anhui University of Science and Technology,Huainan 232001,China)
出处
《浙江大学学报（工学版）》
EI
CAS
CSCD
北大核心
2023年第12期2448-2455,2466,共9页
Journal of Zhejiang University：Engineering Science
基金
安徽理工大学医学专项培育项目(YZ2023H2B006)
安徽理工大学引进人才科研启动基金资助项目(2022yjrc44)
安徽理工大学研究生创新基金资助项目(2022CX2086)
国家自然科学基金资助项目(52174141)
安徽省自然科学基金资助项目(2108085ME158)。
