Artificial intelligence based detection of age-related macular degeneration using optical coherence tomography with unique image preprocessing

Abstract

Purpose The aim of the study is to improve the accuracy of age related macular degeneration (AMD) disease in its earlier phases with proposed Capsule Network (CapsNet) architecture trained on speckle noise reduced spectral domain optical coherence tomography (SD-OCT) images based on an optimized Bayesian non-local mean (OBNLM) filter augmentation techniques. Methods A total of 726 local SD-OCT images were collected and labelled as 159 drusen, 145 dry AMD, 156 wet AMD and 266 normal. Region of interest (ROI) was identified. Speckle noise in SD-OCT images were reduced based on OBNLM filter. The processed images were fed to proposed CapsNet architecture to clasify SD-OCT images. Accuracy rates were calculated in both public and local dataset. Results Accuracy rate of local SD-OCT image dataset classification was achieved to a value of 96.39% after performing data augmentation and speckle noise reduction with OBNLM. The performance of proposed CapsNet was also evaluated on the public Kaggle dataset under the same processing procedures and the accuracy rate was calculated as 98.07%. The sensitivity and specificity rates were 96.72% and 99.98%, respectively. Conclusions The classification success of proposed CapsNet may be improved with robust pre-processing steps like; determination of ROI and denoised SD-OCT images based on OBNLM. These impactful image preprocessing steps yielded higher accuracy rates for determining different types of AMD including its precursor lesion on the both local and public dataset with proposed CapsNet architecture.

Deep learning for detection of age-related macular degeneration: A systematic review and meta-analysis of diagnostic test accuracy studies

“…Bhatia et al [ 41 ] used VGG-16 to classify 5588 OCT images from Mendeley, Duke, Noor, and Self-built datasets (4 classes including AMD, CNV, DME, normal) for AMD identifying with a sensitivity of 94% and a specificity of 90%. Celebi et al [ 42 ] used CapsNet with 7 layers to classify 726 OCT images form Kaggle and self-built datasets (2 classes including AMD and normal) for AMD identifying with a sensitivity of 100% and a specificity of 99%. Dong et al [ 43 ] used a joint CNN detector using Yolov3 to classify 208758 FP images from self-built multicenter real-world data (11 classes including AMD, DR, glaucoma, pathological myopia, retinal vein occlusion, macula hole, epiretinal macular membrane, hypertensive retinopathy, myelinated fibers, retinitis pigmentosa and normal) for AMD identifying with a sensitivity of 88% and a specificity of 98%.…”

“…The detailed limitations for each study were summarized in Table 3 . Generally, 8 studies [ 42 , 47 , 50 – 54 , 56 ] did not study other retinal diseases. 3 studies [ 45 , 49 , 55 ] only contained one other diseases.…”

“…Bhatia et al [ 41 ] ignored bad quality pictures that may cause a generalization issue. Celebi et al [ 42 ] ignored bad quality pictures and patients who had other retinal diseases. Although Dong et al [ 43 ] established a database with 11 classes, the number of retinitis pigmentosa images is small.…”

Deep Learning Applications to Classification and Detection of Age-Related Macular Degeneration on Optical Coherence Tomography Imaging: A Review

“…Images were pre-processed by identifying the region of interest (ROI) and speckle noise reduction based on optimized Bayesian non-local mean (OBNLM) filter. The proposed algorithm was validated on the public Kaggle dataset [ 14 ] and achieved an accuracy of 98.0%, sensitivity of 96.7%, and specificity of 99.9% [ 25 ].…”

Classification of dry and wet macular degeneration based on the ConvNeXT model

“…At present, artificial intelligence technology has made breakthrough progress in the field of macular degeneration, and its application in research is mainly focused on macular degeneration identification and macular degeneration severity grading. Celebi et al (2022) achieved automatic detection of macular degeneration with an accuracy of 96.39%. Serener and Serte (2019) classified macular degeneration with 94% accuracy based on the ResNet18 model.…”