A Novel Hybrid Machine Learning Model for Auto-Classification of Retinal Diseases

Abstract

Automatic clinical diagnosis of retinal diseases has emerged as a promising approach to facilitate discovery in areas with limited access to specialists. We propose a novel visual-assisted diagnosis hybrid model based on the support vector machine (SVM) and deep neural networks (DNNs). The model incorporates complementary strengths of DNNs and SVM. Furthermore, we present a new clinical retina label collection for ophthalmology incorporating 32 retina diseases classes. Using EyeNet, our model achieves 89.73% diagnosis accuracy and the model performance is comparable to the professional ophthalmologists.

arXiv commentAccepted at the Joint ICML and IJCAI Workshop on Computational Biology (ICML-IJCAI WCB) to be held in Stockholm SWEDEN, 2018. Referring to https://sites.google.com/view/wcb2018/accepted-papers?authuser=0

References in corpus (4)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
• VQABQ: Visual Question Answering by Basic Questions
• Robustness Analysis of Visual QA Models by Basic Questions

Cited by in corpus (8)

• Retinal Malady Classification using AI: A novel ViT-SVM combination architecture
• Y-net: Multi-scale feature aggregation network with wavelet structure similarity loss function for single image dehazing
• Contextualized Keyword Representations for Multi-modal Retinal Image Captioning
• Query-controllable Video Summarization
• Auto-Classification of Retinal Diseases in the Limit of Sparse Data Using a Two-Streams Machine Learning Model
• GPT2MVS: Generative Pre-trained Transformer-2 for Multi-modal Video Summarization
• Robust Unsupervised Multi-Object Tracking in Noisy Environments
• Synthesizing New Retinal Symptom Images by Multiple Generative Models