Glaucoma Detection in Latino Population through OCT’s RNFL Thickness Map using MobileNet and Inception V3

Resumen

Glaucoma is the leading cause of irreversible blindness worldwide. It is estimated that over 60 million people around the world have this disease, with only part of them knowing they have it. Timely and early diagnosis is vital to delay/prevent patient blindness. Deep learning (DL) could be a tool for ophthalmologists to give a more informed and objective diagnosis. However, there is a lack of studies that apply DL for glaucoma detection to Latino population. Our contribution is to compare the eectiveness of MobileNet and Inception V3 models to detect cases of glaucoma in Latino patients. To this end, transfer learning was used to retrain previously trained models with images of the retinal nerve fibre layer Thickness Map of Mexican patients, obtained with Optical Coherence Tomography, from a clinic in the northern part of Mexico. Specifically, the Inception V3 model showed slight better average results than the MobileNet model in the case of classifying left eye images. In average, the evaluation results for right eye images were the same for both models. The evaluation results of the MobileNet model for the left eye are: accuracy: 86 %, precision: 87 %, recall: 87 %, and F1 score: 87 %. The evaluation
results of the MobileNet model for the right eye are: accuracy: 90 %, precision: 90 %, recall: 90 %, and F1 score: 90 %. The evaluation results of the Inception V3 model for the left eye are: accuracy: 90 %, precision: 90 %, recall: 90 %, and F1 score: 90 %. The evaluation results of the Inception V3 model for the right eye are: accuracy: 90 %, precision: 90 %, recall: 90 %, and F1 score: 90 %.