Employing deep learning architectures for image-based automatic cataract diagnosis

Abstract

Various eye diseases affect the quality of human life severely and ultimately may result in complete vision loss. Ocular diseases manifest themselves through mostly visual indicators in the early-or mature-stages of the disease by showing abnormalities in optics disc, fovea, or other descriptive anatomical structures of the eye. A cataract is among the most harmful diseases that affect millions of people and the leading cause of public vision impairment. It shows major visual symptoms that can be employed for early detection before the hypermature stage. Automatic diagnosis systems intend to assist ophthalmological experts by mitigating the burden of manual clinical decisions and on health care utilization. In this study, a diagnosis systems based on color fundus images are addressed for cataract disease.Deep learning based models were performed for the automatic identification of cataract diseases. Two pre-trained robust architectures, namely VGGNet and DenseNet were employed to detect abnormalities in descriptive parts of the human eye. The proposed system is implemented on a wide and unique dataset that includes diverse color retinal fundus images, that are acquired comparatively in low-cost and common modality, which is considered as major contribution of the study.The dataset show symptoms of cataracts in different phases and representing the characteristics of the cataract. By the proposed system, dysfunction associated with cataracts could be identified in the early stage. The achievement of the proposed system compared to various traditional and up-to-date classification systems. The proposed system achieves 97.94% diagnosis rate for cataract disease grading.

Utilizing the Ensemble of Deep Learning Approaches to Identify Monkeypox Disease

“…After these processes, models are designed and training/test steps are used. Finally, it is aimed to classify monkeypox disease [19]. Table 1.…”

Iron deficiency anemia detection using machine learning models: A comparative study of fingernails, palm and conjunctiva of the eye images

“…An automated system for detecting cataract disease with the use of color of the fundus images was proposed by Acar et al 35 A deep learning‐based approach, that is, VGGNet and DenseNet architectures was utilized for the study to also detect anomalies for the description of the region of interest (ROI) in the human eyes when extracting the ROI of the conjunctiva of the images. The proposed system archived an accuracy of 97.94% and 95.07% by the VGGNet by DenseNet respectively in the diagnosis of cataract disease.…”