Classification of Retinal Diseases in Optical Coherence Tomography Images Using Artificial Intelligence and Firefly Algorithm

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Özdaş M. B., Uysal F., HARDALAÇ F.

Diagnostics, cilt.13, sa.3, 2023 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 13 Sayı: 3
  • Basım Tarihi: 2023
  • Doi Numarası: 10.3390/diagnostics13030433
  • Dergi Adı: Diagnostics
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, EMBASE, INSPEC, Directory of Open Access Journals
  • Anahtar Kelimeler: biomedical image processing, deep learning, firefly algorithm, hierarchy classification, machine learning, optical coherence tomography
  • Gazi Üniversitesi Adresli: Evet

Özet

© 2023 by the authors.In recent years, the number of studies for the automatic diagnosis of biomedical diseases has increased. Many of these studies have used Deep Learning, which gives extremely good results but requires a vast amount of data and computing load. If the processor is of insufficient quality, this takes time and places an excessive load on the processor. On the other hand, Machine Learning is faster than Deep Learning and does not have a much-needed computing load, but it does not provide as high an accuracy value as Deep Learning. Therefore, our goal is to develop a hybrid system that provides a high accuracy value, while requiring a smaller computing load and less time to diagnose biomedical diseases such as the retinal diseases we chose for this study. For this purpose, first, retinal layer extraction was conducted through image preprocessing. Then, traditional feature extractors were combined with pre-trained Deep Learning feature extractors. To select the best features, we used the Firefly algorithm. In the end, multiple binary classifications were conducted instead of multiclass classification with Machine Learning classifiers. Two public datasets were used in this study. The first dataset had a mean accuracy of 0.957, and the second dataset had a mean accuracy of 0.954.