Cotton-wool spots, red-lesions and hard-exudates distinction using CNN enhancement and transfer learning

Tian-Swee Tan (Universiti Teknologi Malaysia (UTM))
M. A. As'ari (Universiti Teknologi Malaysia (UTM))
Wan Hazabbah Wan Hitam (Universiti Sains Malaysia (USM))
Qi Zhe Ngoo (Universiti Sains Malaysia (USM))
Matthias Tiong Foh thye (Universiti Teknologi Malaysia (UTM))
Kelvin Ling Chia hiik (Universiti Teknologi Malaysia (UTM))

Indonesian Journal of Electrical Engineering and Computer Science
Vol 23, No 2: August 2021

The automatic retinal disease diagnosis by artificial intelligent is an interesting and challenging topic in the medical field. It requires an appropriate image enhancement technique and a sufficient training dataset for the specific retina conditions. The aim of this study was to design an automatic diagnosis convolutional neural network (CNN) model which does not require a large training dataset to specifically identify diabetic retinopathy symptoms, which are cotton wool, exudates spots and red lesionin colour fundus pictures. A novel framework comprised image enhancement method by using upgraded contrast limited adaptive histogram equalization (UCLAHE) filter and transferred pre-trained networks was developed to classify the retinal diseases regarding to the symptoms. The performance of the proposed framework was evaluated based on accuracy, sensitivity and specificity metrics. The collected results have proven the robustness of the proposed framework in offering good accuracy in retina diseases diagnosis.