Author Affiliations
Abstract
Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou 570228, P. R. China
In ophthalmology, retinal optical coherence tomography (OCT) images with noticeable structural features help identify human eyes as healthy or diseased. The recently hot artificial intelligence (AI) realized this recognition process automatically. However, speckle noise in the original retinal OCT image reduces the accuracy of disease classification. This study presents a time-saving approach based on deep learning to improve classification accuracy by removing the noise from the original dataset. Firstly, four pre-trained convolutional neural networks (CNNs) from the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) were trained to classify the original images into two categories: The noise reduction required (NRR) and the noise-free (NF) images. Among the CNNs, VGG19_BN performed best with 98% accuracy and 99% recall. Then, we used the block-matching and 3D filtering (BM3D) algorithm to denoise the NRR images. Those noise-removed NRR and the NF images form the processed dataset. The quality of images in the dataset is prominently ameliorated after denoising, which is valid to improve the models’ performance. The original and processed datasets were tested on the four pre-trained CNNs to evaluate the effectiveness of our proposed approach. We have compared the CNNs, and the results show the performance of the CNNs trained with the processed dataset is improved by an average of 2.04%, 5.19%, and 5.10% under overall accuracy (OA), Macro F1-score, and Micro F1-score, respectively. Especially for DenseNet161, the OA is improved to 98.14%. Our proposed method demonstrates its effectiveness in improving classification accuracy and opens a new solution to reduce denoising time-consuming for large datasets.
Optical coherence tomography deep learning retinal disease classification Journal of Innovative Optical Health Sciences
2023, 16(6): 2350008
山西大学物理电子工程学院, 山西 太原 030006
实验研究了铯蒸气中受激简并四波混频(DFWM)产生的一对共轭光束的增益谱。结果表明,在简并二能级跃迁系统中,只有当基态的角动量大于或等于激发态的角动量时,才能发生基于受激DFWM的光放大效应。在考虑原子的所有塞曼子能态后,定性理论分析表明,该过程是在稳定的基态塞曼相干且初始塞曼子能级上有大量原子布居的前提下,基于多个N型跃迁环路实现的。并进一步分析了泵浦功率、原子数密度和单光子失谐等实验参量对DFWM信号增益的影响。此外,研究发现,额外引入一束波长为852 nm的抽运光,可显著提高DFWM的放大效率。
量子光学 光放大 简并四波混频 电磁诱导透明 基态相干 抽运光 光学学报
2021, 41(18): 1827001