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Early Detection of Diabetic Retinopathy Using Machine Intelligence throughDeep Transfer and Representational Learning 被引量:2
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作者 Fouzia Nawaz Muhammad Ramzan +3 位作者 Khalid Mehmood Hikmat Ullah khan saleem hayat khan Muhammad Raheel Bhutta 《Computers, Materials & Continua》 SCIE EI 2021年第2期1631-1645,共15页
Diabetic retinopathy (DR) is a retinal disease that causes irreversible blindness.DR occurs due to the high blood sugar level of the patient, and it is clumsy tobe detected at an early stage as no early symptoms appea... Diabetic retinopathy (DR) is a retinal disease that causes irreversible blindness.DR occurs due to the high blood sugar level of the patient, and it is clumsy tobe detected at an early stage as no early symptoms appear at the initial level. To preventblindness, early detection and regular treatment are needed. Automated detectionbased on machine intelligence may assist the ophthalmologist in examining thepatients’ condition more accurately and efficiently. The purpose of this study is toproduce an automated screening system for recognition and grading of diabetic retinopathyusing machine learning through deep transfer and representational learning.The artificial intelligence technique used is transfer learning on the deep neural network,Inception-v4. Two configuration variants of transfer learning are applied onInception-v4: Fine-tune mode and fixed feature extractor mode. Both configurationmodes have achieved decent accuracy values, but the fine-tuning method outperformsthe fixed feature extractor configuration mode. Fine-tune configuration modehas gained 96.6% accuracy in early detection of DR and 97.7% accuracy in gradingthe disease and has outperformed the state of the art methods in the relevant literature. 展开更多
关键词 Diabetic retinopathy artificial intelligence automated screening system machine learning deep neural network transfer and representational learning
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