DOI: 10.5281/zenodo.6528966
title: Neuronal Transfer Networks (Trainings)

description: Training versions of the AlexNet Convolutional Neural Network (CNN), related to the evaluation of eye diseases. -The following datasets were used in this work: <strong>LAG. </strong>Contains fundus images with positive and negative glaucoma samples obtained from Beijing Tongren Hospital. Each fundus image is diagnosed by qualified glaucoma specialists, taking into consideration of both morphologic and functional analysis. https://arxiv.org/abs/1903.10831 <strong>APTOS. </strong>Contains images of diabetic retinopathy that were used in the APTOS 2019 blindness screening competitions. Each image has been resized and cropped to have a maximum size of 1024px. A certified clinician rated each image according to the severity of diabetic retinopathy on a scale of 0 to 4. https://www.kaggle.com/c7934597/resized-2015-2019-diabetic-retinopathy-detection/metadata/ <strong>HRF. </strong>Contains 15 images of healthy patients, 15 images of patients with diabetic retinopathy and 15 images of glaucomatous patients. They were captured by a Canon CR-1 fundus camera with a field of view of 45 degrees with a resolution of 3504×2336 px. https://www5.cs.fau.de/research/data/fundus-images/ <strong>ODIR. </strong>Contains images of 5000 patients with various eye diseases collected by Shanggong Medical Technology Co., Ltd. from different hospitals/medical centers in China. The fundus images are captured with various cameras on the market, resulting in varied image resolutions. They classify patients into eight labels based on the images of both eyes. https://odir2019.grand-challenge.org/dataset/ <strong>sjchoi86-HRF. </strong>This dataset contains 601 fundus images divided into 4 groups: normal (300 images), glaucoma (101 images), cataract (100 images) and retina disease (100 images). https://github.com/cvblab/retina_dataset -Specifications of the data used for training in each version of the Convolutional Neural Network (CNN): netTransfer: Based on glaucoma and non-glaucoma imag-es cases existing in the LAG-database. netTransfer2: Based on glaucoma and non-glaucoma imag-es cases existing in the LAG-database and the sjchoi86-HRF database (this data-base also contains images on other pathologies, but only the glaucoma samples were used for this training) netTransfer3: Based on glaucoma, diabetic retinopathy and non-disease images cases existing in the LAG-database, sjchoi86-HRF database and the HRF database. netTransfer4: Based on glaucoma, diabetic retinopathy and non-disease images cases existing in the LAG-database, sjchoi86-HRF database, HRF database and the APTOS database. However, data from the fourth iteration was corrupted and then there for lost. netTransfer5: Based on glaucoma, diabetic retinopathy and non-disease images cases existing in the LAG-database, sjchoi86-HRF database, HRF database and the APTOS database (Images were cropped slightly before training). netTransfer6: Based on glaucoma, diabetic retinopa-thy and non-disease images cases existing in the LAG-database, sjchoi86-HRF database, HRF database, APTOS database and ODIR database (this database also contains images on several other diseases, but only the glaucoma and diabetic retinopathy samples were used for this training; furthermore, cropping was used for the images in order the eliminate black borders). <strong>PLEASE CITATE AS:</strong> Arias-Serrano I, Velásquez-López PA, Avila-Briones LN <em>et al.</em> Artificial intelligence based glaucoma and diabetic retinopathy detection using MATLAB — retrained AlexNet convolutional neural network [version 1; peer review: 1 approved with reservations]. <em>F1000Research</em> 2023, <strong>12</strong>:14 (https://doi.org/10.12688/f1000research.122288.1)