Improving Classification Efficiency Based on Combination of Extreme Gradient Boosting and Deep Transfer Learning

• Main Authors: Phankokkruad, Manop, Wacharawichanant, Sirirat
• Format: Conference Proceeding
• Language: English
• Subjects: Cataracts; Classification; Computational modeling; Deep learning; Diabetic Ratinopathy; Glaucoma; Macular degeneration; Ocular Disease; ResNet50; Sensitivity; Training; Transfer learning; Xception; XGBoost
• ISSN: 2837-7109
• DOI: 10.1109/ICCC59590.2023.10507377

The leading causes of blindness and low vision are ocular disease. Ocular disease such as glaucoma, cataract, diabetic retinopathy, and macular degeneration, which are diseases in which the risk of vision loss. Unfortunately, some ocular diseases have no symptoms until the late stages. Therefore, early-stage diagnosis of ocular disease is the best way to prevent vision loss. This work proposed the classification models for ocular disease classification using XGBoost in combination with deep transfer learning of CNN as the feature extractor. In the model training process, we used the pre-trained model including Xception and ResNet50 based on the transfer learning technique to extract different features. The proposed model was used to classify ocular disease into eight patterns. The XGBoost in combination with the ResNet50 model achieved an accuracy level of 87.82%, precision of 88.15%, sensitivity of 87.82%, and F1 score of 87.82%. The XGBoost in combination with the Xception model acquired an accuracy level of 87.02%, precision of 87.35%, sensitivity of 87.02%, and F1 score of 87.02%. By considering the F1 score, XGBoost in combination with transfer learning of CNN models gave a high score. Therefore, all evaluation parameters clearly indicate the high performance of the ocular disease classification model. The conclusion presents that the proposed method acquires more excellent performance than individual deep learning models.