مقاله انگلیسی رایگان در مورد استفاده از تصاویر فوندوس شبکیه برای شناسایی گلوکوم به کمک کامپیوتر – الزویر ۲۰۲۲

مقاله انگلیسی رایگان در مورد استفاده از تصاویر فوندوس شبکیه برای شناسایی گلوکوم به کمک کامپیوتر – الزویر ۲۰۲۲

 

مشخصات مقاله
ترجمه عنوان مقاله تشخیص گلوکوم به کمک کامپیوتر مبتنی بر هوش مصنوعی با استفاده از تصاویر فوندوس شبکیه
عنوان انگلیسی مقاله Artificial Intelligence-based computer-aided diagnosis of glaucoma using retinal fundus images
انتشار مقاله سال ۲۰۲۲
تعداد صفحات مقاله انگلیسی ۲۵ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه الزویر
نوع نگارش مقاله
مقاله پژوهشی (Research Article)
مقاله بیس این مقاله بیس میباشد
نمایه (index) Scopus – Master Journal List – JCR
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
۹٫۶۰۲ در سال ۲۰۲۰
شاخص H_index ۲۲۵ در سال ۲۰۲۲
شاخص SJR ۲٫۰۷۰ در سال ۲۰۲۰
شناسه ISSN ۰۹۵۷-۴۱۷۴
شاخص Quartile (چارک) Q1 در سال ۲۰۲۰
فرضیه ندارد
مدل مفهومی دارد
پرسشنامه ندارد
متغیر ندارد
رفرنس دارد
رشته های مرتبط مهندسی کامپیوتر – پزشکی
گرایش های مرتبط مهندسی نرم افزار – هوش مصنوعی – چشم پزشکی – بینایی سنجی
نوع ارائه مقاله
ژورنال
مجله  سیستم های خبره با برنامه های کاربردی – Expert Systems with Applications
دانشگاه Division of Electronics and Electrical Engineering, Dongguk University, Republic of Korea
کلمات کلیدی هوش مصنوعی – کاپ اپتیک و تقسیم دیسک بینایی – غربالگری گلوکوم – تشخیص به کمک کامپیوتر – SLS-Net و SLSR-Net
کلمات کلیدی انگلیسی Artificial intelligence – Optic cup and optic disc segmentation – Glaucoma screening – Computer-aided diagnosis – SLS-Net and SLSR-Net
شناسه دیجیتال – doi
https://doi.org/10.1016/j.eswa.2022.117968
کد محصول e16765
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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فهرست مطالب مقاله:
Abstract
۱٫ Introduction
۲٫ Related work
۳٫ Proposed methods
۴٫ Experimental results
۵٫ Conclusion
CRediT authorship contribution statement
Declaration of Competing Interest
Acknowledgments
References

بخشی از متن مقاله:

Abstract

     Glaucoma is one of the most common chronic diseases that may lead to irreversible vision loss. The number of patients with permanent vision loss due to glaucoma is expected to increase at an alarming rate in the near future. A considerable amount of research is being conducted on computer-aided diagnosis for glaucoma. Segmentation of the optic cup (OC) and optic disc (OD) is usually performed to distinguish glaucomatous and non-glaucomatous cases in retinal fundus images. However, the OC boundaries are quite non-distinctive; consequently, the accurate segmentation of the OC is substantially challenging, and the OD segmentation performance also needs to be improved. To overcome this problem, we propose two networks, separable linked segmentation network (SLS-Net) and separable linked segmentation residual network (SLSR-Net), for accurate pixel-wise segmentation of the OC and OD. In SLS-Net and SLSR-Net, a large final feature map can be maintained in our networks, which enhances the OC and OD segmentation performance by minimizing the spatial information loss. SLSR-Net employs external residual connections for feature empowerment. Both proposed networks comprise a separable convolutional link to enhance computational efficiency and reduce the cost of network. Even with a few trainable parameters, the proposed architecture is capable of providing high segmentation accuracy.

Introduction

     Glaucoma has become one of the major causes of vision loss, and in this disease, the optic nerve head (ONH) is damaged (Tham et al., 2014). Glaucoma causes gradual vision loss, and the patient has no abrupt considerable symptoms; hence, its early detection and screening are crucial. Many advanced imaging methods are employed by experts for retinal disease diagnosis and assessment. Fundus imaging is widely used in glaucoma detection tasks because it is fast, affordable, and non-invasive (Edupuganti et al., 2018). Color fundus imaging best serves the glaucoma detection in both advanced glaucoma or early glaucoma detection cases (Ahn et al., 2018). Fundus imaging also enables researchers and experts for computational analysis like cup-to-disc ratio (CDR) computation which significantly helps in glaucoma detection (Orlando et al., 2020).

     Several methods have been used for the assessment of glaucoma; however, owing to numerous clinical and resource problems, they could not fill the gap of its early diagnosis (Baum et al., 1995). Compared to other methods, the ONH assessment is more commonly used. Automated ONH assessment methods are gaining popularity over manual methods these days because the

Conclusion

     In this research, we proposed deep learning-based novel models, SLS-Net and SLSR-Net, to segment OD and OC for glaucoma screening. SLSR-Net is the final proposed model that maintains a large final feature map size throughout the network to avoid spatial information loss of even minor features. Memory requirements are one of the major limitations of computer-aided diagnosis. An SCL unit in our model minimizes this problem and significantly increases the computational efficiency of the network. External residual connections settle the feature degradation problem by empowering the features. Training and testing of the network is carried out without any preprocessing or postprocessing overhead. We extensively evaluated the proposed model on four publicly available datasets and achieved state-of-the-art performances compared with existing methods. There is a trade-off between segmentation performance and computational efficiency. Therefore, methods that achieve good segmentation performance usually use many parameters, which makes the network computationally expensive. In our network, good results are achieved by using only 4,666,950 trainable parameters, which confirms the outstanding computational efficiency of the network compared to the state-of-the-art methods.

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