A Deep Learning Approach to Automatic Recognition of Arcus Senilis
| العنوان: | A Deep Learning Approach to Automatic Recognition of Arcus Senilis |
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| المؤلفون: | Ali Ameri, Nasrin Amini |
| المصدر: | Journal of Biomedical Physics and Engineering, Vol 10, Iss 4, Pp 507-512 (2020) Journal of Biomedical Physics & Engineering |
| بيانات النشر: | Shiraz University of Medical Sciences, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Feature engineering, lcsh:Medical physics. Medical radiology. Nuclear medicine, Computer science, lcsh:R895-920, 0206 medical engineering, Bioengineering, 02 engineering and technology, transfer learning, 03 medical and health sciences, 0302 clinical medicine, medicine, Radiology, Nuclear Medicine and imaging, Segmentation, In patient, Iris (anatomy), Training set, Radiological and Ultrasound Technology, business.industry, Deep learning, Arcus senilis, deep learning, Pattern recognition, 020601 biomedical engineering, medicine.anatomical_structure, classification, Original Article, Artificial intelligence, arcus senilis, medicine.symptom, business, Transfer of learning, 030217 neurology & neurosurgery |
| الوصف: | Background: Arcus Senilis (AS) appears as a white, grey or blue ring or arc in front of the periphery of the iris, and is a symptom of abnormally high cholesterol in patients under 50 years old. Objective: This work proposes a deep learning approach to automatic recognition of AS in eye images.Material and Methods: In this analytical study, a dataset of 191 eye images (130 normal, 61 with AS) was employed where ¾ of the data were used for training the proposed model and ¼ of the data were used for test, using a 4-fold cross-validation. Due to the limited amount of training data, transfer learning was conducted with AlexNet as the pretrained network. Results: The proposed model achieved an accuracy of 100% in classifying the eye images into normal and AS categories. Conclusion: The excellent performance of the proposed model despite limited training set, demonstrate the efficacy of deep transfer learning in AS recognition in eye images. The proposed approach is preferred to previous methods for AS recognition, as it eliminates cumbersome segmentation and feature engineering processes. |
| اللغة: | English |
| تدمد: | 2251-7200 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6015b38bd9a49c6b040c10bf18e5ea2c https://jbpe.sums.ac.ir/article_46594_c7a5f8be89d77f2acde9b3fbf65ac97a.pdf |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....6015b38bd9a49c6b040c10bf18e5ea2c |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 22517200 |
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