{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T17:58:38Z","timestamp":1790272718139,"version":"4.1.0"},"reference-count":45,"publisher":"IOP Publishing","issue":"6","license":[{"start":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T00:00:00Z","timestamp":1762819200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T00:00:00Z","timestamp":1762819200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Biomed. Phys. Eng. Express"],"published-print":{"date-parts":[[2025,11,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Retinal diseases are the leading causes of visual impairment, and early diagnosis is essential for treatment. Optical coherence tomography (OCT), a non-invasive imaging technique, provides high-resolution images for retinal disease classification; however, its complexity and the limitations of manual diagnosis require efficient automated classification methods. To enableearly clinical diagnosis, this paper proposes a Convolutional Block Attention Module (CBAM)- Automatic Mixing Precision (AMP) network (CANet) for automated classification of retinal images. The model categorizes diabetic macular oedema (DME), choroidal neovascularisation (CNV), drusen. and normal cases, which is built upon the ResNet-34 architecture. CBAM is introduced into the residual block to propose the CBAM-Block residual block embedded in the ResNet-34 network model, which combines the channel and spatial attention mechanism to enhance the lesion feature extraction capability. AMP is used to accelerate the training and combine with transfer learning to enhance the model generalization. Meanwhile, median filtering, normalization, dynamic thresholding to remove white edges and data enhancement are used to optimize data quality and alleviate the problem of category imbalance. Classification experiments were performed on the OCT-2017 dataset for the four categories and ablation experiments were performed to demonstrate their effectiveness. The total classification accuracy of the model reaches 0.9890, the AUC value of all categories is 1, where the recall of CNV reaches 1. CBAM, AMP, and transfer learning improve the classification accuracy by 0.9%, 1.6%, and 9.4%, respectively, and the ablation experiments likewise prove that the model remains highly robust to noisy data. The experimental results show that the CANet model significantly improves the OCT image classification performance through multi-module integration, which provides an efficient and reliable technical solution for the automated diagnosis of retinal diseases.<\/jats:p>","DOI":"10.1088\/2057-1976\/ae161b","type":"journal-article","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T22:47:38Z","timestamp":1761173258000},"page":"065045","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["An OCT retinal image classification model based on improved ResNet-34 network"],"prefix":"10.1088","volume":"11","author":[{"given":"Zhenwei","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6054-7872","authenticated-orcid":true,"given":"Jiawen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Angchao","family":"Duan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayi","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenchen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2025,11,11]]},"reference":[{"key":"bpexae161bbib1","doi-asserted-by":"publisher","first-page":"876927","DOI":"10.3389\/fninf.2022.876927","type":"journal-article","article-title":"FN-OCT: disease detection algorithm for retinal optical coherence tomography based on a fusion network","volume":"16","author":"Ai","year":"2022","journal-title":"Frontiers in Neuroinformatics"},{"key":"bpexae161bbib2","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1167\/tvst.12.1.29","type":"journal-article","article-title":"Deep learning-based system for disease screening and pathologic region detection from optical coherence tomography images","volume":"12","author":"Chen","year":"2023","journal-title":"Translational Vision Science & Technology"},{"key":"bpexae161bbib3","doi-asserted-by":"publisher","first-page":"104810","DOI":"10.1016\/j.bspc.2023.104810","type":"journal-article","article-title":"Classification and segmentation of OCT images for age-related macular degeneration based on dual guidance networks","volume":"84","author":"Diao","year":"2023","journal-title":"Biomed. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2025-05-09","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-10-22","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-11-11","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}