Multimodality Fusion Strategies in Eye Disease Diagnosis.
Multimodality fusion has gained significance in medical applications, particularly in diagnosing challenging diseases like eye diseases, notably diabetic eye diseases that pose risks of vision loss and blindness. Mono-modality eye disease diagnosis proves difficult, often missing crucial disease ind...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 5; pp. 2524 - 2559 |
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| Autores principales: | , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=181515409&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 181515409 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2024 vid: 37 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 181515409 181515409 181515409 10.1007/s10278-024-01105-x 181515409 ppf: 2524 ppct: 35 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Multimodality Fusion Strategies in Eye Disease Diagnosis. aug: au: El-Ateif, Sara Idri, Ali affil: https://ror.org/00r8w8f84 Software Project Management Research Team, ENSIAS, Mohammed V University, BP 713, Agdal, Rabat, Morocco sug: subj: Eye Diseases Diagnosis Diabetic Retinopathy Diagnosis Angiography Methods Diagnostic Imaging Methods Retina Pathology Human Image Processing, Computer Assisted Methods Neural Networks (Computer) Descriptive Statistics Diagnostic Errors Prevention and Control Comparative Studies ab: Multimodality fusion has gained significance in medical applications, particularly in diagnosing challenging diseases like eye diseases, notably diabetic eye diseases that pose risks of vision loss and blindness. Mono-modality eye disease diagnosis proves difficult, often missing crucial disease indicators. In response, researchers advocate multimodality-based approaches to enhance diagnostics. This study is a unique exploration, evaluating three multimodality fusion strategies—early, joint, and late—in conjunction with state-of-the-art convolutional neural network models for automated eye disease binary detection across three datasets: fundus fluorescein angiography, macula, and combination of digital retinal images for vessel extraction, structured analysis of the retina, and high-resolution fundus. Findings reveal the efficacy of each fusion strategy: type 0 early fusion with DenseNet121 achieves an impressive 99.45% average accuracy. InceptionResNetV2 emerges as the top-performing joint fusion architecture with an average accuracy of 99.58%. Late fusion ResNet50V2 achieves a perfect score of 100% across all metrics, surpassing both early and joint fusion. Comparative analysis demonstrates that late fusion ResNet50V2 matches the accuracy of state-of-the-art feature-level fusion model for multiview learning. In conclusion, this study substantiates late fusion as the optimal strategy for eye disease diagnosis compared to early and joint fusion, showcasing its superiority in leveraging multimodal information. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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