Retinal Arteriosclerosis Detection Based on Improved VGG-16 Network
Doctors can diagnose retinal arteriosclerosis by observing the fundus image. However, the workload is large. This process is inefficient and accordingly unsuitable for the screening of retinal arteriosclerosis in large population. To address this issue, the current study proposes a
retinal arteriosclerosis detection method on the basis of an improved VGG-16 network. First, the proposed method extracts multiscale features on the basis of the VGG-16 network by utilizing a dense block to enhance feature transfers and applying them to the segmentation of fundus arteriovenous
vessels and arterial reflective bands. Test results reveal that the segmentation accuracy, sensitivity and specificity of the proposed method is 92.9%, 85.21%, and 93.40%, respectively. Subsequently, the effective region of the reflective band is screened, and a four-segment Gaussian model
is utilized to fit the grayscale of the cross section of arterial blood vessel and reflective band in the effective region. Finally, the proposed method quantitatively detects retinal arteriosclerosis and calculates the reflective parameters of arterial blood vessels and reflective bands,
such as bandwidth and grayscale ratios. A patient with retinal arteriosclerosis can be determined on the basis of the reflective parameters. The average detection accuracy of retinal arteriosclerosis is 92.3%, which is in accordance with the requirements of large-scale retinal arteriosclerosis.
Keywords: GAUSSIAN FITTING; RETINAL ARTERIOSCLEROSIS DETECTION; RETINAL FUNDUS IMAGES; VGG-16 NETWORK
Document Type: Research Article
Publication date: 01 December 2019
- Journal of Medical Imaging and Health Informatics (JMIHI) is a medium to disseminate novel experimental and theoretical research results in the field of biomedicine, biology, clinical, rehabilitation engineering, medical image processing, bio-computing, D2H2, and other health related areas.
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