A deep learning framework for the early detection of multi-retinal diseases

• Published in: PloS one Vol. 19; no. 7; p. e0307317
• Main Authors: Ejaz, Sara, Baig, Raheel, Ashraf, Zeeshan, Alnfiai, Mrim M., Alnahari, Mona Mohammed, Alotaibi, Reemiah Muneer
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 25.07.2024; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Artificial neural networks; Automation; Biology and Life Sciences; Cameras; Care and treatment; Cataracts; Color; Color vision; Computer and Information Sciences; Data augmentation; Data mining; Datasets; Deep Learning; Diabetes; Diabetes mellitus; Diabetic retinopathy; Diabetic Retinopathy - diagnosis; Diabetic Retinopathy - diagnostic imaging; Diagnosis; Diagnostic systems; Disease; Early Diagnosis; Eye; Eye diseases; Feature extraction; Fundus Oculi; Glaucoma; Health services; Humans; Image processing; Image Processing, Computer-Assisted - methods; Machine learning; Macular degeneration; Medical imaging; Medical imaging equipment; Medical research; Medical treatment; Medicine and Health Sciences; Neural networks; Neural Networks, Computer; Ophthalmology; Optic nerve; Performance prediction; Photography; Research and Analysis Methods; Retina; Retina - diagnostic imaging; Retina - pathology; Retinal Diseases - diagnosis; Retinal Diseases - diagnostic imaging; Retinal images; Retinopathy; Simultaneous discrimination learning; Social Sciences; Statistical models; Visual impairment
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0307317

Retinal images play a pivotal contribution to the diagnosis of various ocular conditions by ophthalmologists. Extensive research was conducted to enable early detection and timely treatment using deep learning algorithms for retinal fundus images. Quick diagnosis and treatment planning can be facilitated by deep learning models’ ability to process images rapidly and deliver outcomes instantly. Our research aims to provide a non-invasive method for early detection and timely eye disease treatment using a Convolutional Neural Network (CNN). We used a dataset Retinal Fundus Multi-disease Image Dataset (RFMiD), which contains various categories of fundus images representing different eye diseases, including Media Haze (MH), Optic Disc Cupping (ODC), Diabetic Retinopathy (DR), and healthy images (WNL). Several pre-processing techniques were applied to improve the model’s performance, such as data augmentation, cropping, resizing, dataset splitting, converting images to arrays, and one-hot encoding. CNNs have extracted extract pertinent features from the input color fundus images. These extracted features are employed to make predictive diagnostic decisions. In this article three CNN models were used to perform experiments. The model’s performance is assessed utilizing statistical metrics such as accuracy, F1 score, recall, and precision. Based on the results, the developed framework demonstrates promising performance with accuracy rates of up to 89.81% for validation and 88.72% for testing using 12-layer CNN after Data Augmentation. The accuracy rate obtained from 20-layer CNN is 90.34% for validation and 89.59% for testing with Augmented data. The accuracy obtained from 20-layer CNN is greater but this model shows overfitting. These accuracy rates suggested that the deep learning model has learned to distinguish between different eye disease categories and healthy images effectively. This study’s contribution lies in providing a reliable and efficient diagnostic system for the simultaneous detection of multiple eye diseases through the analysis of color fundus images.