Automatic Diabetic Retinopathy Diagnosis Using Adaptive Fine-Tuned Convolutional Neural Network

• Published in: IEEE access 2021, Vol.9, p.41344-41359
• Main Authors: Saeed, Fahman, Hussain, Muhammad, Aboalsamh, Hatim A.
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Blindness; classification; CNN; Diabetes; Diabetic retinopathy; Diagnosis; Feature extraction; Fundus images; Lesions; Machine learning; Medical imaging; Neural networks; Principal components analysis; Retina; Retinopathy; Training; Transfer learning
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3065273

Diabetic retinopathy (DR) is a complication of diabetes that leads to blindness. The manual screening of color fundus images to detect DR at early stages is expensive and time consuming. Deep learning (DL) techniques have been employed for automatic DR screening on fundus images due to their outstanding performance in many applications. However, training a DL model needs a huge amount of data, which are usually unavailable in the case of DR, and overfitting is unavoidable. Employing a two-stage transfer learning method, we developed herein an intelligent computer-aided system using a pre-trained convolutional neural network (CNN) for automatic DR screening on fundus images. A CNN model learns the domain-specific hierarchy of low- to high-level features. Given this, using the regions of interest (ROIs) of lesions extracted from the annotated fundus images, the first layer of a pre-trained CNN model is re-initialized. The model is then fine-tuned, such that the low-level layers learn the local structures of the lesion and normal regions. As the fully connected layer (FC) layers encode high-level features, which are global in nature and domain specific, we replace them with a new FC layer based on the principal component analysis PCA and use it in an unsupervised manner to extract discriminate features from the fundus images. This step reduces the model complexity, significantly avoiding the overfitting problem. This step also lets the model adopt the fundus image structures, making it suitable for DR feature detection. Finally, we add a gradient boosting-based classification layer. The evaluation of the proposed system using a 10-fold cross-validation on two challenging datasets (i.e., EyePACS and Messidor) indicates that it outperforms state-of-the-art methods. It will be useful for the initial screening of DR patients and will help graders in deciding quickly as regards patient referral to an ophthalmologist for further diagnosis and treatment.