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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Wan Hazabbah Wan Hitam
Universiti Sains Malaysia (USM)

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Cotton-wool spots, red-lesions and hard-exudates distinction using CNN enhancement and transfer learning Tian-Swee Tan; M. A. As'ari; Wan Hazabbah Wan Hitam; Qi Zhe Ngoo; Matthias Tiong Foh thye; Kelvin Ling Chia hiik
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp1170-1179

Abstract

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. 
Convolution neural network model for fundus photograph quality assessment Sinan S. Mohammed Sheet; Tian-Swee Tan; Muhammad Amir Bin As'ari; Wan Hazabbah Wan Hitam; Qi Zhe Ngoo; Joyce S.Y. Sia; Kelvin Ling Chia Hiik
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp915-923

Abstract

The excellent quality of color fundus photograph is crucial for the ophthalmologist to process the correct diagnosis and for convolutional neural network (CNN) models to optimize output classification. As a result of main causes as acquire devises efficiency and experience of a physician most fundus photographs can have uneven illuminance, blur, and bad contrast, in addition to micro-features of retinal diseases, which need to force their contrast. Fundus photograph quality assessment method is proposed to find out the perfect enhanced color fundus Technique in fundoscopy photographs-based CNN model. Five photograph quality measurements, in addition to five CNN metrics, were used as standard in this study. In this research innovative approach combining photograph quality measurement and CNN metrics analysis is proposed to find out the best enhance method that is set for the multiclass CNN model. The contrast enhancement techniques are evaluated using 267 color fundus photographs divided into three retina diseases cases were downloaded from the open-source database “FIGSHARE”. The study outcome showed that the presented system (single-CNN) can determine well the contrast enhancement method, as well as the low-quality fundus photograph then it can boost CNN metrics to achieve superior.
Co-Authors Joyce S.Y. Sia Kelvin Ling Chia hiik M. A. As'ari Matthias Tiong Foh thye Muhammad Amir Bin As'ari Qi Zhe Ngoo Qi Zhe Ngoo Sinan S. Mohammed Sheet Tian-Swee Tan