Artificial Humming Bird Optimization–Based Hybrid CNN-RNN for Accurate Exudate Classification from Fundus Images.
Diabetic retinopathy is the predominant cause of visual impairment in diabetes patients. The early detection process can prevent diabetes patients from severe situations. The progression of diabetic retinopathy is determined by analyzing the fundus images, thus determining whether they are affected...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 1; pp. 59 - 73 |
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| Autores principales: | , , , |
| Formato: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2023
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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=162233259&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 162233259 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2023 vid: 36 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 162233259 159651100 162233259 162233259 10.1007/s10278-022-00707-7 162233259 ppf: 59 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Artificial Humming Bird Optimization–Based Hybrid CNN-RNN for Accurate Exudate Classification from Fundus Images. aug: au: E., Dhiravidachelvi S., Senthil Pandi R., Prabavathi C., Bala Subramanian affil: Department of Electronics and Communication Engineering, Mohamed sathak engineering college, Kilakarai, Tamil Nadu, India sug: subj: Diagnosis, Computer Assisted Methods Diabetic Retinopathy Diagnosis Neural Networks (Computer) Algorithms Early Diagnosis Methods Sensitivity and Specificity Human Validity ROC Curve Descriptive Statistics Image Processing, Computer Assisted Digital Imaging Diabetic Retinopathy Classification Comparative Studies False Positive Results ab: Diabetic retinopathy is the predominant cause of visual impairment in diabetes patients. The early detection process can prevent diabetes patients from severe situations. The progression of diabetic retinopathy is determined by analyzing the fundus images, thus determining whether they are affected by exudates or not. The manual detection process is laborious and requires more time and there is a possibility of wrong predictions. Therefore, this research focuses on developing an automated decision-making system. To predict the existence of exudates in fundus images, we developed a novel technique named a hybrid convolutional neural network-recurrent neural network along with the artificial humming bird optimization (HCNNRNN-AHB) approach. The proposed HCNNRNN-AHB technique effectively detects and classifies the fundus image into two categories namely exudates and non-exudates. Before the classification process, the optic discs are removed to prevent false alarms using Hough transform. Then, to differentiate the exudates and non-exudates, color and texture features are extracted from the fundus images. The classification process is then performed using the HCNNRNN-AHB approach which is the combination of CNN and RNN frameworks along with the AHB optimization algorithm. The AHB algorithm is introduced with this framework to optimize the parameters of CNN and RNN thereby enhancing the prediction accuracy of the model. Finally, the simulation results are performed to analyze the effectiveness of the proposed method using different performance metrics such as accuracy, sensitivity, specificity, F-score, and area under curve score. The analytic result reveals that the proposed HCNNRNN-AHB approach achieves a greater prediction and classification accuracy of about 97.4%. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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