Advanced Image Enhancement and a Lightweight Feature Pyramid Network for Detecting Microaneurysms in Diabetic Retinopathy Screening

IF 3 4区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Muhammad Zeeshan Tahir, Xingzheng Lyu, Muhammad Nasir, Sanyuan Zhang
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Abstract

Diabetic retinopathy (DR) is a complication of diabetes that can lead to vision impairment and even permanent blindness. The increasing number of diabetic patients and a shortage of ophthalmologists highlight the need for automated screening tools for early detection. Microaneurysms (MAs) are the earliest indicators of DR. However, detecting MAs in fundus images is a challenging task due to its small size and subtle features. Additionally, low contrast, noise, and lighting variations in fundus images, such as glare and shadows, further complicate the detection process. To address these challenges, we incorporated image enhancement techniques such as green channel utilization, gamma correction, and median filtering to improve image quality. Furthermore, to enhance the performance of the MA detection model, we employed a lightweight feature pyramid network (FPN) with a pretrained ResNet34 backbone to capture multiscale features and the convolutional block attention module (CBAM) to enhance feature selection. CBAM applies spatial and channel-wise attention, which allows the model to focus on the most relevant features for improved detection. We evaluated our method on the IDRID and E-ophtha datasets, achieving a sensitivity of 0.607 and F1 score of 0.681 on IDRID and a sensitivity of 0.602 and F1 score of 0.650 on E-ophtha. These experimental results show that our proposed method gives better results than previous methods.

先进图像增强和轻量化特征金字塔网络在糖尿病视网膜病变筛查中检测微动脉瘤
糖尿病视网膜病变(DR)是糖尿病的一种并发症,可导致视力损害甚至永久性失明。糖尿病患者数量的增加和眼科医生的短缺突出了对早期检测自动化筛查工具的需求。微动脉瘤(micro动脉瘤,MAs)是dr的最早指标,但由于其体积小且特征微妙,在眼底图像中检测其是一项具有挑战性的任务。此外,眼底图像中的低对比度、噪声和光照变化(如眩光和阴影)进一步使检测过程复杂化。为了解决这些挑战,我们结合了图像增强技术,如绿色通道利用、伽马校正和中值滤波来提高图像质量。此外,为了提高MA检测模型的性能,我们采用了带有预训练ResNet34主干的轻量级特征金字塔网络(FPN)来捕获多尺度特征,并采用了卷积块注意模块(CBAM)来增强特征选择。CBAM应用空间和通道关注,这使得模型能够专注于最相关的特征,以改进检测。我们对IDRID和E-ophtha数据集进行了评估,IDRID的敏感性为0.607,F1评分为0.681,E-ophtha的敏感性为0.602,F1评分为0.650。实验结果表明,本文提出的方法比以往的方法具有更好的效果。
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来源期刊
International Journal of Imaging Systems and Technology
International Journal of Imaging Systems and Technology 工程技术-成像科学与照相技术
CiteScore
6.90
自引率
6.10%
发文量
138
审稿时长
3 months
期刊介绍: The International Journal of Imaging Systems and Technology (IMA) is a forum for the exchange of ideas and results relevant to imaging systems, including imaging physics and informatics. The journal covers all imaging modalities in humans and animals. IMA accepts technically sound and scientifically rigorous research in the interdisciplinary field of imaging, including relevant algorithmic research and hardware and software development, and their applications relevant to medical research. The journal provides a platform to publish original research in structural and functional imaging. The journal is also open to imaging studies of the human body and on animals that describe novel diagnostic imaging and analyses methods. Technical, theoretical, and clinical research in both normal and clinical populations is encouraged. Submissions describing methods, software, databases, replication studies as well as negative results are also considered. The scope of the journal includes, but is not limited to, the following in the context of biomedical research: Imaging and neuro-imaging modalities: structural MRI, functional MRI, PET, SPECT, CT, ultrasound, EEG, MEG, NIRS etc.; Neuromodulation and brain stimulation techniques such as TMS and tDCS; Software and hardware for imaging, especially related to human and animal health; Image segmentation in normal and clinical populations; Pattern analysis and classification using machine learning techniques; Computational modeling and analysis; Brain connectivity and connectomics; Systems-level characterization of brain function; Neural networks and neurorobotics; Computer vision, based on human/animal physiology; Brain-computer interface (BCI) technology; Big data, databasing and data mining.
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