Document Type : Reseach Article

Authors

• Marwah M. Mahdi ¹
• Mohammed Abdulkreem Mohammed ²
• Haider Al-Chalibi ³
• Bashar S. Bashar ⁴
• Hayder Adnan Sadeq ⁵
• Talib Mohammed Jawad Abbas ⁶

¹ Anesthesia Techniques Department, Al-Mustaqbal University College, Babylon, Iraq

² Department of Anesthesia Techniques, Al-Noor University College, Bartella, Iraq

³ Medical Technical College, Al-Farahidi University, Baghdad, Iraq

⁴ Al-Nisour University College, Baghdad, Iraq

⁵ Al-Hadi University College, Baghdad,10011, Iraq

⁶ Medical device engineering, Ashur University College, Baghdad, Iraq

Abstract

To stop vision loss from glaucoma, early identification and regular screening are crucial. Convolutional neural networks (CNN) have been effectively used in recent years to diagnose glaucoma automatically from color fundus pictures. CNNs can extract distinctive characteristics directly from the fundus pictures, as opposed to the current automatic screening techniques. In this study, a CNN-based deep learning architecture is created for the categorization of normal and glaucomatous fundus pictures. In this paper, we propose a deep learning-based framework for the detection of glaucoma based on retinal images. Our proposed approach utilizes the two CNN-based models, namely Inception and DenseNet, in order to classify the input images. We also show the impact of transfer learning on the training and the validation processes and put forward an effective pipeline with lower trainable parameters for the target task. Our experiments on a collected dataset demonstrate the efficacy of the proposed model by achieving an accuracy of 93.84%, a precision of 92.83%, and a recall of 95.00%.

Keywords

• Glaucoma Detection
• Convolutional Neural Networks
• Medical Images Analysis
• Retinal Images
• DenseNet
• Inception