Effects of Open-Source Image Preprocessing on Glaucoma and Glaucoma Suspect Fundus Image Differentiation with CNN

Abstract

Purpose: Investigating whether a Keras-based Convolutional Neural Networks (CNN) model could detect glaucoma suspect cases from glaucoma cases without a visual field test and the effects of open-source data preprocessing in AI based glaucoma detection.Methods: 398 glaucoma and 378 glaucoma suspect cases approved by a glaucoma specialist ophthalmologist were enrolled in this study. Fundus images were retrieved from an optical coherence tomography device. An open-source graphic software was used to create training sets. There were three distinct groups: fundus-centered cropped images, grayscale version of said images with an auto white balance option to enhance the features and in addition to the conversions stated above, horizontal, vertical, and horizontal plus vertical flips were applied in the third group. Cropped images were used to train our Keras-based CNN model with 49 deep layers. Model fit was designed at 50 epochs per run and the performance metrics for each run were recorded. Normality was assessed with the Shapiro-Wilk test. A one-way ANOVA was applied to analyze each image set's validation accuracy. Bonferroni corrections were applied if appropriate. Demographics of the patients were analyzed with the Mann-Whitney-U test and the chi-2 test.Results: The mean age of glaucoma patients and glaucoma suspected patients showed a statistically significant difference (P-value < 0.001, mean ± standard deviation 62 ± 15 and 45 ± 15 respectively). Gender did not show a statistically significant difference between groups (P-value = 0.388). Validation accuracy scores in groups 1,2 and 3 were 0.71 ± 0.02, 0.77± 0.02 and 0.85±0.03 respectively (mean ± standard deviation) (P-value < 0.001). The sensitivities and specificities between groups were different and those differences were found to be statistically significant (P-value < 0.001 and P-value < 0.001, respectively).Conclusion: In this report, we introduce open-source and easy-to-deploy image pre-processing methods to improve the outcome of glaucoma detection from glaucoma suspected cases in stereoscopic optic disc photography-derived fundus images that could be used with any CNN-based computer-aided diagnosis system without requiring a visual field test, contributing to decreasing the burden associated with undiagnosed glaucoma progression.

A Novel Fusion Deep Learning Approach for Retinal Disease Diagnosis Enhanced by Web Application Predictive Tool

“…Upon evaluating the combined data from all the datasets, the model demonstrated a noteworthy accuracy rate of 78%. In an independent study, Seker et al [13] developed a CNN model using Keras framework for the purpose of glaucoma classification. The fundus images were first subjected to preprocessing using the Irfanview graphic schemes prior to being inputted into the classification pipeline.…”

“…Methods Accuracy Arslan et al [7] EfficientNet 94.88% Malik et al [8] Random Forest 86.63% Metin and Karasulu [9] ResNet50 94.00% Sarki et al [10] CNN 81.33% Hussain et al [11] Random Forest 96.89% Almansour et al [12] Fine-tuned VGG16 78.00% Seker et al [13] Keras-based CNN 85.00% Barai et al…”