An Approach Towards Automatic Detection of Toxoplasmosis using Fundus Images

Chakravarthy A. D., Abeyrathna D., Subramaniam M., Chundi P., Halim M. S., Hasanreisoglu M., ...Daha Fazla

19th Annual IEEE International Conference on Bioinformatics and Bioengineering (BIBE), Athens, Yunanistan, 28 - 30 Ekim 2019, ss.710-717 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Doi Numarası: 10.1109/bibe.2019.00134
  • Basıldığı Şehir: Athens
  • Basıldığı Ülke: Yunanistan
  • Sayfa Sayıları: ss.710-717
  • Anahtar Kelimeler: Neural Networks, Deep Learning, Medical Imaging, Ocular Toxoplasmosis, DIABETIC-RETINOPATHY, VALIDATION
  • Gazi Üniversitesi Adresli: Evet

Özet

Ocular Toxoplasmosis (OT) is a widespread infectious chorioretinal disease whose timely diagnosis and treatment are crucial to prevent potential vision loss. Diagnosing OT is a challenging task ranging from tedious analyses of fundus images of the eye to serological clinical tests. An automated approach using convolutional neural networks (CNNs) towards diagnosing OT by analyzing fundus images is described. Fundus images are segmented to patches using a sliding window and are classified into healthy and unhealthy fundus image patches using a CNN model. An OT lesion heat map of a fundus image is generated from these patches. The heat map and patch features are then combined to develop a dual input hybrid CNN model detecting OT fundus images with high accuracy. The approach was applied to a dataset of fundus images involving OT and normal subjects and was highly effective in identifying fundus images having OT lesions.