Deep CNN with Hybrid Binary Local Search and Particle Swarm Optimizer for Exudates Classification from Fundus Images.
Diabetic retinopathy is a chronic condition that causes vision loss if not detected early. In the early stage, it can be diagnosed with the aid of exudates which are called lesions. However, it is arduous to detect the exudate lesion due to the availability of blood vessels and other distractions. T...
| Publicado en: | Journal of Digital Imaging Vol. 35; no. 1; pp. 56 - 68 |
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| Autores principales: | , , |
| Formato: | algorithm diagnostic images tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2022
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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=155312941&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155312941 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2022 vid: 35 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 155312941 154530342 155312941 155312941 10.1007/s10278-021-00534-2 155312941 ppf: 56 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep CNN with Hybrid Binary Local Search and Particle Swarm Optimizer for Exudates Classification from Fundus Images. aug: au: Ramya, J. Rajakumar, M. P. Maheswari, B. Uma affil: Computer Science and Engineering, St. Joseph's College of Engineering, OMR, Chennai, India sug: subj: Exudates and Transudates Neural Networks (Computer) Digital Imaging Diabetic Retinopathy Augmented Reality Image Processing, Computer Assisted ab: Diabetic retinopathy is a chronic condition that causes vision loss if not detected early. In the early stage, it can be diagnosed with the aid of exudates which are called lesions. However, it is arduous to detect the exudate lesion due to the availability of blood vessels and other distractions. To tackle these issues, we proposed a novel exudates classification from the fundus image known as hybrid convolutional neural network (CNN)-based binary local search optimizer–based particle swarm optimization algorithm. The proposed method from this paper exploits image augmentation to enlarge the fundus image to the required size without losing any features. The features from the resized fundus images are extracted as a feature vector and fed into the feed-forward CNN as the input. Henceforth, it classifies the exudates from the fundus image. Further, the hyperparameters are optimized to reduce the computational complexities by utilization of binary local search optimizer (BLSO) and particle swarm optimization (PSO). The experimental analysis is conducted on the public ROC and real-time ARA400 datasets and compared with the state-of-art works such as support vector machine classifiers, multi-modal/multi-scale, random forest, and CNN for the performance metrics. The classification accuracy is high for the proposed work, and thus, our proposed outperforms all the other approaches. pubtype: Academic Journal doctype: algorithm diagnostic images tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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