Early-stage prediction of glaucoma disease to reduce surgical requirements using deep-learning

Abstract

Glaucoma is an emerging retinal disease which may become the first common cause of blindness if not detected at earlier stage. Retinal examination used by ophthalmologists for diagnosis is tedious and time consuming, thus computer vision is been progressively used for earlier diagnosis employing retinal images. Researchers these days are using different machine learning and deep learning approaches for diagnosis. As, the machine learning techniques require extraction of handcrafted features for classification, use of deep learning is found to be promising for automated diagnosis. This paper presents the comparative analysis of different state of the art deep learning techniques such as Xception, Inception, DenseNet, ResNet and VGG. Further, the comparison of approaches is performed using parameters such as precision, recall and accuracy. The outcome of this study could be employed for designing of handheld diagnostic tools of glaucoma that can be used by medical practitioners and researchers for analysis of retinal images and prediction of glaucoma. As a result, the diagnosis performed using Computer aided diagnosis (CAD) systems using imaging modalities would perform better in presence of illuminating disturbances as well and reduce diagnostic time and cost performed by conventional devices such as tonometer, pachymeter for retinal examination. Additionally, the life of handheld diagnostic device would increase due to ease of use, and recurrent use of retinal cameras for acquisition of fundus image of same eye would reduce due to improved prediction at single instance. Further, these systems would help to predict glaucoma at earlier stage, plan treatment using medications and reduce the number of surgeries. Now, if the disease is predicted at earlier stage, this would in turn save the patient from surgery performed at advanced stage, thereby optimizing the use of materials such as stainless steel and titanium used for designing of surgical equipment.