Performance evaluation of various deep learning based models for effective glaucoma evaluation using optical coherence tomography images

Abstract

Glaucoma is the dominant reason for irreversible blindness worldwide, and its best remedy is early and timely detection. Optical coherence tomography has come to be the most commonly used imaging modality in detecting glaucomatous damage in recent years. Deep Learning using Optical Coherence Tomography Modality helps in predicting glaucoma more accurately and less tediously. This experimental study aims to perform glaucoma prediction using eight different ImageNet models from Optical Coherence Tomography of Glaucoma. A thorough investigation is performed to evaluate these models’ performances on various efficiency metrics, which will help discover the best performing model. Every net is tested on three different optimizers, namely Adam, Root Mean Squared Propagation, and Stochastic Gradient Descent, to find the best relevant results. An attempt has been made to improvise the performance of models using transfer learning and fine-tuning. The work presented in this study was initially trained and tested on a private database that consists of 4220 images (2110 normal optical coherence tomography and 2110 glaucoma optical coherence tomography). Based on the results, the four best-performing models are shortlisted. Later, these models are tested on the well-recognized standard public Mendeley dataset. Experimental results illustrate that VGG16 using the Root Mean Squared Propagation Optimizer attains auspicious performance with 95.68% accuracy. The proposed work concludes that different ImageNet models are a good alternative as a computer-based automatic glaucoma screening system. This fully automated system has a lot of potential to tell the difference between normal Optical Coherence Tomography and glaucomatous Optical Coherence Tomography automatically. The proposed system helps in efficiently detecting this retinal infection in suspected patients for better diagnosis to avoid vision loss and also decreases senior ophthalmologists’ (experts) precious time and involvement.

Robodoc: a conversational-AI based app for medical conversations

“…Subramanian et al (2022) utilized transfer learning and Bayesian optimization to classify retinal diseases from OCT images, achieving high accuracy with DenseNet201. Singh et al (2022) proposed an integrated deep learning framework for accelerated OCT angiography.…”

Clinical Perspectives on the Use of Computer Vision in Glaucoma Screening

“…Deep learning (DL) technology associated with OCT demonstrates efficiency, precision, and exemplary performance in interpreting the exam and discriminating glaucomatous eyes from normal eyes [ 22 , 23 ], contributing to improving diagnosis and reducing the precious time and involvement of professional experts [ 24 ].…”

“…Singh [ 24 ] suggested a tool to evaluate DL models applied in OCT to evaluate glaucoma, using standard and glaucomatous OCT images. Experts referenced the results.…”

Gonioscopia diagnóstica e cirúrgica. Atualidades e perspectivas

“…O uso de inteligência artificial pode potencialmente impactar positivamente o uso dessa tecnologia na avaliação do ângulo da câmara anterior. (21)(22)(23) O registo das imagens do exame de gonioscopia pode ser feito por intermédio de uma câmera digital acoplada à lâmpada de fenda/ microscópio cirúrgico ou por um equipamento específico para isto, o Gonioscope GS-1 NIDEK. (4) O uso da câmera acoplada à lâmpada de fenda/ microscópio cirúrgico só permite o registro do exame tradicional da gonioscopia, ou seja, exige a mesma habilidade necessária para o exame sem a câmera.…”