Cotton-wool spots, red-lesions and hard-exudates distinction using CNN enhancement and transfer learning
| Autor: | Matthias Tiong Foh Thye, Muhammad Amir As'ari, Kelvin Ling Chia Hiik, Qi Zhe Ngoo, Tian-Swee Tan, Wan Hazabbah Wan Hitam |
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| Rok vydání: | 2021 |
| Předmět: |
Control and Optimization
Computer Networks and Communications business.industry Computer science media_common.quotation_subject Pattern recognition Diabetic retinopathy Filter (signal processing) Fundus (eye) medicine.disease Convolutional neural network Cotton wool spots Hardware and Architecture Robustness (computer science) Signal Processing medicine Contrast (vision) Adaptive histogram equalization Artificial intelligence Electrical and Electronic Engineering medicine.symptom business Information Systems media_common |
| Zdroj: | Indonesian Journal of Electrical Engineering and Computer Science. 23:1170 |
| ISSN: | 2502-4760 2502-4752 |
| Popis: | The automatic retinal disease diagnosis by artificial intelligent is an interesting and challenging topic in the medical field. It requires an appropriate image enhancement technique and a sufficient training dataset for the specific retina conditions. The aim of this study was to design an automatic diagnosis convolutional neural network (CNN) model which does not require a large training dataset to specifically identify diabetic retinopathy symptoms, which are cotton wool, exudates spots and red lesionin colour fundus pictures. A novel framework comprised image enhancement method by using upgraded contrast limited adaptive histogram equalization (UCLAHE) filter and transferred pre-trained networks was developed to classify the retinal diseases regarding to the symptoms. The performance of the proposed framework was evaluated based on accuracy, sensitivity and specificity metrics. The collected results have proven the robustness of the proposed framework in offering good accuracy in retina diseases diagnosis. |
| Databáze: | OpenAIRE |
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