An optimized deep learning based technique for grading and extraction of diabetic retinopathy severities

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Zhang Q., Luo J., CENGİZ K.

Informatica (Slovenia), cilt.45, sa.5, ss.659-665, 2021 (ESCI, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 45 Sayı: 5
  • Basım Tarihi: 2021
  • Doi Numarası: 10.31449/inf.v45i5.3561
  • Dergi Adı: Informatica (Slovenia)
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, Aerospace Database, Applied Science & Technology Source, Biotechnology Research Abstracts, Central & Eastern European Academic Source (CEEAS), Communication Abstracts, Compendex, Computer & Applied Sciences, Metadex, zbMATH, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.659-665
  • Anahtar Kelimeler: Convolution neural network, Deep learning, Diabetic retinopathy, Machine learning, Severity grading
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Trakya Üniversitesi Adresli: Evet

Özet

The prognosis of Diabetic Retinopathy (DR) requires regular eye examinations, as ophthalmologists depends on fundus segmentation to treat DR pathologies. Automated approaches for detection, segmentation and classification have developed as an imperative area of research for the effective diagnosis of DR for the treatment of serious eye conditions that prevent visual impairment. Diagnosis of various DR lesions, as well as different severities, helping the ophthalmologists to analyze variations in fundus images and take the necessary measures before the disease progresses. Deep learning techniques have evolved as a recent advent to combat the issues of conventional machine leaning based methods. An optimized deep learning framework is proposed in this article for grading and extraction of diabetic retinopathy severities. This involves various steps like background segmentation, feature set extraction, feature optimization using Cuckoo search and Convolutional Neural Network (CNN) severity grade classification. The method was validated on two standard datasets MESSIDOR and IDRiD. The proposed method yields an accuracy value of 97.55%, cross entropy loss of 0.367 and time intricacy of 20 mins and 15 secs for MESSIDOR and 98.02% cross entropy loss of 0.345 and time intricacy of 22 mins and 21 secs for IDRiD dataset; respectively. The state-of-the-art comparison depicts that the proposed CNN based method provides a maximum accuracy improvement of 10.46% comparative to the existing methodology. The proposed framework yields better accuracy by procurement of the investigative outcomes acquired exhibits proficient DR determination.