Retinal disease identification using upgraded CLAHE filter and transfer convolution neural network
| Autor: | Muhammad Amir As'ari, Wan Hazabbah Wan Hitam, Joyce Sin Yin Sia, Sinan S. Mohammed Sheet, Tian-Swee Tan |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Computer Networks and Communications
Computer science Noise reduction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Normalization (image processing) 02 engineering and technology Convolutional neural network chemistry.chemical_compound Artificial Intelligence Histogram 0202 electrical engineering electronic engineering information engineering business.industry 020208 electrical & electronic engineering 020206 networking & telecommunications Pattern recognition Retinal Filter (signal processing) eye diseases chemistry Hardware and Architecture Adaptive histogram equalization Artificial intelligence Transfer of learning business Software Information Systems |
| Zdroj: | ICT Express. 8:142-150 |
| ISSN: | 2405-9595 |
| Popis: | Retinal tissue plays a crucial part in human vision. Infections of retinal tissue and delayed treatment or untreated infection could lead to loss of vision. Additionally, the diagnosis is prone to errors when huge dataset is involved. Therefore, a fully automated model of identification of retinal disease is proposed to reduce human interaction while retaining its high accuracy classification results. This paper introduces an enhanced design of a fully automatic multi-class retina diseases prediction system to assist ophthalmologists in making speedy and accurate investigation. Retinal fundus images, which have been used in this study, were downloaded from the stare website (157 images from five classes: BDR, CRVO, CNV, PDR, and Normal). The five files were categorized according to their annotations conducted by the experienced specialists. The categorized images were first processed with the proposed upgraded contrast-limited adaptive histogram filter for image brightness enhancement, noise reduction, and intensity spectrum normalization. The proposed model was designed with transfer learning method and the fine-tuned pre-trained RESNET50. Eventually, the proposed framework was examined with performance evaluation parameters, recorded a classification rate with 100% sensitivity, 100% specificity, and 100% accuracy. The performance of the proposed model showed a magnificent superiority as compared to the state-of-the-art studies. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|