RETRACTED ARTICLE: An optimal approach to detect retinal diseases by performing segmentation of retinal blood vessels using image processing

Sreemathy, J.; Arun, Anupama; Aruna, M.; Vigneshwaran, P.

doi:10.1007/s00500-023-08526-w

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…In recent years, image enhancement techniques have been largely investigated, especially in the field of medical imaging. Many traditional approaches use histogram equalization [12,13], sharpening filtering [14], super-resolution [15,16], Retinex [17], etc., to enhance the image, while others apply homomorphic filtering [18], and wavelet transform [19,20] to process the image in the frequency domain. Several more recent works have shown that CNN can successfully demonstrate better performance and efficiency compared with traditional methods in image enhancement [21,22].…”

Section: Introductionmentioning

confidence: 99%

UR-Net: An Integrated ResUNet and Attention Based Image Enhancement and Classification Network for Stain-Free White Blood Cells

Zheng,

Huang,

Chen

et al. 2023

Sensors

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The differential count of white blood cells (WBCs) can effectively provide disease information for patients. Existing stained microscopic WBC classification usually requires complex sample-preparation steps, and is easily affected by external conditions such as illumination. In contrast, the inconspicuous nuclei of stain-free WBCs also bring great challenges to WBC classification. As such, image enhancement, as one of the preprocessing methods of image classification, is essential in improving the image qualities of stain-free WBCs. However, traditional or existing convolutional neural network (CNN)-based image enhancement techniques are typically designed as standalone modules aimed at improving the perceptual quality of humans, without considering their impact on advanced computer vision tasks of classification. Therefore, this work proposes a novel model, UR-Net, which consists of an image enhancement network framed by ResUNet with an attention mechanism and a ResNet classification network. The enhancement model is integrated into the classification model for joint training to improve the classification performance for stain-free WBCs. The experimental results demonstrate that compared to the models without image enhancement and previous enhancement and classification models, our proposed model achieved a best classification performance of 83.34% on our stain-free WBC dataset.

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