Detection of proliferative diabetic retinopathy in fundus images using convolution neural network

• Main Authors: Hasliza, Abu Hassan, Marzuqi, Yaakob, Sasni, Ismail, Juwairiyyah, Abd Rahman, Izyani, Mat Rusni, Azlee, Zabidi, Ihsan, Mohd Yassin, Nooritawati, Md Tahir, Suraiya, Mohamad Shafie
• Format: Conference or Workshop Item
• Language: English
• Published: Institute of Physics Publishing 2020
• Subjects: QA75 Electronic computers. Computer science; QA76 Computer software; T Technology (General); TA Engineering (General). Civil engineering (General)
• Online Access: http://umpir.ump.edu.my/id/eprint/37364/1/Detection%20of%20proliferative%20diabetic%20retinopathy%20in%20fundus%20images%20using%20convolution%20neural%20network.pdf; http://umpir.ump.edu.my/id/eprint/37364/; https://doi.org/10.1088/1757-899X/769/1/012029
• Institution: Universiti Malaysia Pahang

Convolution Neural Network (CNN) is one of the techniques under Artificial Neural Network (ANN) used to develop a Deep Learning Neural Network (DLNN) algorithm for detection of Proliferative Diabetic Retinopathy (PDR) on the fundus images. About 116 PDR and 150 Non-Proliferative Diabetic Retinopathy (NPDR) of fundus images retrieved from the publicly available MESSIDOR database applied in this research. This study consisted three objectives that included the execution of two pre-processing techniques on the data-set which were resizing and normalizing the fundus images, developed deep learning operational Artificial Intelligence (AI) network of feature extraction algorithm for detection of PDR on the fundus images and determined the output classification of the network encompassing the accuracy, sensitivity and specificity. There were five different parameters carried out along this research. Here, Parameter 5 showed the best performance among the five parameters based on the value of accuracy, sensitivity, and specificity that was 73.81%, 76%, and 69% respectively.