Glaucoma is one of the most severe eye diseases, characterized by rapid progression and leading to irreversible blindness. It is often the case that diagnostics is carried out when one's sight has already significantly degraded due to the lack of noticeable symptoms at early stage of the disease. Regular glaucoma screenings of the population shall improve early-stage detection, however the desirable frequency of etymological checkups is often not feasible due to the excessive load imposed by manual diagnostics on limited number of specialists. Considering the basic methodology to detect glaucoma is to analyze fundus images for the optic-disc-to-optic-cup ratio, Machine Learning algorithms can offer sophisticated methods for image processing and classification. In our work, we propose an advanced image pre-processing technique combined with a multi-view network of deep classification models to categorize glaucoma. Our Glaucoma Automated Retinal Detection Network (GARDNet) has been successfully tested on Rotterdam EyePACS AIROGS dataset with an AUC of 0.92, and then additionally fine-tuned and tested on RIM-ONE DL dataset with an AUC of 0.9308 outperforming the state-of-the-art of 0.9272. Our code is available on https://github.com/ahmed1996said/gardnet
翻译:Glaucoma是眼疾中最为严重的疾病之一,其特点是快速发展并导致不可逆转的失明;经常发生的情况是,当人们的视力因疾病早期缺乏明显症状而明显退化时,即进行诊断;定期的青光谱人口筛查应改善早期检测,然而,由于人工诊断对数量有限的专家施加过重的负担,导致体温检查的适宜频率往往不可行;考虑到检测青光谱的基本方法是分析光学-分光-光学-显像比率的基金图象,机器学习算法可以提供复杂的图像处理和分类方法;在我们的工作中,我们提议采用先进的图像处理前技术,结合一个深度分类模型的多视图网络,对青光谱进行分类;我们的Glaoucoma自动Retinal检测网络(GARDNet)已经成功地在鹿特丹-EyePACS AIROGS数据集进行了测试,并随后对RIM-ONE DL数据处理和分类分类方法进行了进一步调整和测试;在我们的工作中,我们用0.98/MASASAS AS AS AS AS AN ASU AS ASU ASU ASUT ASUDERAS 。