{"doi":"10.56726/irjmets47245","title":"Diagnosis of Retinal Diseases from OCT Images using Deep Learning Algorithms","abstract":"OCT is a non-invasive test that provides ophthalmologists with cross-sectional images of the retinal layer of the eye, enabling them to make diagnoses based on the layers of the retina.As a result, it is a crucial modality for the identification and measurement of disorders and anomalies of the retina.For each patient, OCT produces several images, therefore ophthalmologists must spend a lot of time analyzing the data.In this paper, four categories-such as Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), Drusen, and Normal-are proposed for machine learning models that classify OCT pictures of patients.There are two distinct models put forth.Convolutional neural networks (CNNs) with three layers are used in one, whereas those with seven layers are used in the other to create binary classifiers, several CNNs are modified as feature extractors, including VGG16, ResNet50, and DenseNet121.With 0.927 accuracy, 0.927 sensitivity, and 0.932 specificity, the suggested model that uses VGG16 for CNV vs.Other classes, DME vs.Other classes, Drusen vs.Other classes, and Normal vs.Other classes exhibit the best performance among them.The accuracy of the Normal class binary classifier is 0.947.These findings indicate that they could serve as an additional reader for ophthalmologists.","journal":"International Research Journal of Modernization in Engineering Technology and Science","year":2023,"id":415083,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9187,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[],"reference_count":16,"raw_metadata":null,"created_at":"2026-07-19T01:22:08.814145Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}